Functional Magnetic Resonance Imaging

Number: 0739

Table Of Contents

Policy
Applicable CPT / HCPCS / ICD-10 Codes
Background
References


Policy

Aetna considers functional magnetic resonance imaging (fMRI) medically necessary to identify the eloquent cortex in pre-surgical evaluation of persons with brain tumors (except temporal tumors), epilepsy (except temporal neocortical epilepsy), or vascular malformations.

Aetna considers fMRI experimental and investigational to identify the eloquent cortex in pre-surgical evaluation of persons with temporal neocortical epilepsy or temporal tumors.

Aetna considers fMRI experimental and investigational for the diagnosis, monitoring, prognosis, or surgical management of all other indications, including any of the following conditions/diseases (not an all-inclusive list) because its effectiveness for these indications has not been established:

  • Alzheimer's disease
  • Anger and aggressive behaviors
  • Anxiety disorder
  • Anoxic-ischemic brain injury
  • Attention-deficit hyperactivity disorder
  • Autism spectrum disorder
  • Bipolar disorder
  • Childhood mal-treatment
  • Chronic pain (including fibromyalgia)
  • Disorders of consciousness (e.g., locked-in syndrome, minimally conscious state (subacute/chronic; traumatic/non-traumatic), and coma/vegatative state)
  • Emotion-expressive suppression
  • Multiple sclerosis
  • Obsessive-compulsive disorder
  • Panic disorder
  • Parkinson's disease
  • Psychosis
  • Psychotic depression
  • Schizophrenia
  • Sleep behavior disorder
  • Stroke/stroke rehabilitation
  • Trauma (e.g., head injury).

See also CPB 0279 - Magnetic Source Imaging / Magnetoencephalography.


Table:

CPT Codes / HCPCS Codes / ICD-10 Codes

Code Code Description

Information in the [brackets] below has been added for clarification purposes.   Codes requiring a 7th character are represented by "+":

CPT codes covered if selection criteria are met:

70554 Magnetic resonance imaging, brain, functional MRI; including test selection and administration of repetitive body part movement and/or visual stimulation, not requiring physician or psychologist administration
70555     requiring physician or psychologist administration of entire neurofunctional testing

ICD-10 codes covered if selection criteria are met:

C71.0 - C71.9 Malignant neoplasm of brain
C79.31 - C79.49 Secondary malignant neoplasm of brain and other parts of nervous system
D33.0 - D33.2 Benign neoplasm of brain
D43.0 - D43.2, D43.4 Neoplasm of uncertain behavior of brain and spinal cord
G40.001 - G40.919 Epilepsy and recurrent seizures
Q28.2 - Q28.3 Arteriovenous and other malformations of cerebral vessels
R56.1 Post traumatic seizures
R56.9 Unspecified convulsions

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):

F20.0 - F20.9
F25.0 - F25.9
Schizophrenia
F29 Unspecified psychosis not due to a substance or known physiological condition
F30.10 - F31.9 Bipolar I disorder
F32.3 Major depressive disorder, single episode, severe with psychotic features
F33.3 Major depressive disorder, recurrent, severe with psychotic symptoms
F41.0 - F41.9 Other anxiety disorders
F42.2 - F42.9 Obsessive-compulsive disorder
F84.0 - F84.9 Pervasive developmental disorders
F90.1 - F90.9 Attention-deficit hyperactivity disorder,
G20 - G21.9 Parkinson's disease and secondary parkinsonism
G30.0 - G30.9 Alzheimer's disease
G35 Multiple sclerosis
G45.0 - G45.2
G45.4 - G45.9
Transient cerebral ischemic attacks and related syndromes
G47.52 REM sleep behavior disorder
G89.21 - G89.29 Chronic pain
G93.1 Anoxic brain damage, not elsewhere classified
G93.81 - G93.89 Other specified disorders of brain [Locked-in syndrome]
I63.00 - I66.9 Cerebral infarction, occlusion and stenosis of cerebral and precerebral arteries not resulting in cerebral infarction
I67.1 - I69.998 Cerebovascular diseases and disorders
M79.7 Fibromyalgia
R40.20 - R40.236+ Coma
R40.3 Persistent vegetative state
R45.4 Irritability and anger
R45.6 Violent behavior
R45.86 Emotional lability
R45.89 Other symptoms and signs involving emotional state
S01.00x+ - S02.92x+ Open wound of head and fracture of skull and facial bones
S05.20x+ - S05.92x+ Injury of eye and orbit
S06.0X0A - S06.A1XS, S06.0XAA - S06.9XAS Intracranial injury
S08.0xx+ - S08.89x+ Avulsion and traumatic amputation of part of head
S09.20x+ - S09.90x+ Other and unspecified injuries of head
T74.12xA - T74.12xS Child physical abuse, confirmed
T74.92xA - T74.92xS Unspecified child maltreatment, confirmed
T76.12xA - T76.12xS Child physical abuse, suspected
T76.92xA - T76.92xS Unspecified child maltreatment

Background

Functional magnetic resonance imaging (fMRI) is a type of functional brain imaging technology.  It localizes regions of activity in the brain by measuring blood flow and/or metabolism following task activation, and is generally used to identify the eloquent cortex in the brain. Eloquent cortex refers to specific cortical areas in the brain that directly controls function, such as language (e.g., Broca's area, Wernicke's area) and sensorimotor function (e.g., sensorimotor cortex).  Injury, dissection or removal of that area can result in major focal neurological deficits (Byrne, 2016). Functional MRI has been used to map these areas when planning a tumor resection. Furthermore, fMRI has been employed for the diagnosis, monitoring, prognosis, or surgical management of many diseases/conditions (e.g., Alzheimer's disease, brain tumors, epilepsy, multiple sclerosis (MS), Parkinson's disease, stroke, trauma, vascular malformations, and vegetative state/coma).

The bulk of published evidence concerning the clinical applications of fMRI centers on its use in pre-surgical planning.  In particular, studies involving language fMRI mainly address its use in pre-surgical planning for epilepsy, arterio-venous malformations (AVMs), and brain tumors (Bookheimer, 2007).  It has been suggested that fMRI of the brain reduces the need for invasive testing of seizure disorder patients being considered for surgical treatment.  Woermann et al (2003) compared the determination of language dominance using fMRI with results of the Wada test in 100 patients with different localization-related epilepsies.  These investigators found 91 % concordance between both tests.  The overall rate of false categorization by fMRI was 9 %, ranging from 3 % in left-sided temporal lobe epilepsy (TLE) to 25 % in left-sided extra-temporal epilepsy.  The authors noted that language fMRI might reduce the necessity of the Wada test for language lateralization, especially in TLE.

Sabsevitz and colleagues (2003) examined whether pre-operative fMRI predicts language deficits in patients with epilepsy undergoing left anterior temporal lobectomy (L-ATL).  A total of 24 patients with L-ATL underwent pre-operative language mapping with fMRI, pre-operative intra-carotid sodium amobarbital (Amytal)/Wada testing for language dominance, as well as pre- and post-operative neuropsychological testing.  Functional MRI laterality indexes (LIs), reflecting the inter-hemispheric difference between activated volumes in left and right homologous regions of interest, were calculated for each patient.  Relationships between the fMRI LI, Wada language dominance, and naming outcome were examined.  Both the fMRI LI (p < 0.001) and the Wada test (p < 0.05) were predictive of naming outcome.  Functional MRI showed 100 % sensitivity and 73 % specificity in predicting significant naming decline.  Both fMRI and the Wada test were more predictive than age at seizure onset or pre-operative naming performance.  The authors concluded that pre-operative fMRI predicted naming decline in patients undergoing L-ATL surgery.

Medina et al (2005) prospectively evaluated effect of fMRI on diagnostic work-up and treatment planning in patients with seizure disorders who are candidates for surgical treatment.  A total of 60 consecutively enrolled patients (27 females and 33 males; mean age of 15.8 +/- 8.7 years; range of  6.8 to 44.2 years) were examined.  Forty-five (75 %) patients were right-handed, 9 (15 %) were left-handed, and 6 (10 %) had indeterminate hand dominance.  Prospective questionnaires were used to evaluate diagnostic work-up, counseling, and treatment plans of the seizure team before and after fMRI.  Confidence level scales were used to determine effect of fMRI on diagnostic and therapeutic thinking.  Paired-t test and 95 % confidence interval analyses were performed.  In 53 patients, language mapping was performed; in 33, motor mapping; and in 7, visual mapping.  The study revealed change in anatomical location or lateralization of language-receptive area -- (Wernicke's area) (28 % of patients) as well as language-expressive area (Broca's area) (21 % of patients).  Statistically significant increases were found in confidence levels after fMRI in regard to motor and visual cortical function evaluation.  In 35 (58 %) of 60 patients, the seizure team thought that fMRI results altered patient and family counseling.  In 38 (63 %) of 60 patients, fMRI results helped to avoid further studies, including the Wada test.  In 31 (52 %) and 25 (42 %) of 60 patients, intra-operative mapping and surgical plans, respectively, were altered because of fMRI results.  In 5 (8 %) patients, two-stage surgery with extra-operative direct electrocortical stimulation mapping (ESM) was averted, and resection was accomplished in one-stage.  In 4 (7 %) patients, extent of surgical resection was altered because eloquent areas were identified close to seizure focus.  The authors concluded that fMRI results influenced diagnostic and therapeutic decision making of the seizure team; results indicated a change in language dominance, an increase in confidence level in identification of critical brain function areas, alterations in patient and family counseling as well as intra-operative mapping and surgical approach.

Functional MRI has been used in pre-surgical planning for patients with brain tumors as well as vascular malformations.  Pouratian and colleagues (2002) evaluated the utility of pre-operative fMRI to predict if a given cortical area would be deemed essential for language processing by ESM.  These investigators studied patients with vascular malformations, specifically AVMs and cavernous angiomas, in whom blood-flow patterns are abnormal and in whom a perfusion-dependent mapping signal may be questionable.  A total of 10 patients were studied (7 with AVMs and 3 with cavernous angiomas).  These researchers used a battery of linguistic tasks, including visual object naming, word generation, auditory responsive naming, visual responsive naming, and sentence comprehension, to identify brain regions that were consistently activated across expression and comprehension linguistic tasks.  In a comparison of ESM and fMRI activations, the researchers varied the matching criteria (overlapping activations, adjacent activations, and deep activations) and the radii of influence of ESM (2.5, 5, and 10 mm) to determine the effects of these factors on the sensitivity and specificity of fMRI.  The sensitivity and specificity of fMRI were dependent on the task, lobe, and matching criterion.  For the population studied, the sensitivity and specificity of fMRI activations during expressive linguistic tasks were found to be up to 100 % and 66.7 %, respectively, in the frontal lobe, and during comprehension linguistic tasks up to 96.2 % and 69.8 %, respectively, in the temporal and parietal lobes.  The sensitivity and specificity of each disease population (AVMs and cavernous angiomas) and of individuals were consistent with those values reported for the entire population studied.  The authors concluded that pre-operative fMRI is a highly sensitive pre-operative planning tool for identifying cortical areas that are essential for language; and that this imaging modality may play a future role in pre-surgical planning for patients with vascular malformations.

Anderson et al (2006) examined the utility of fMRI as a determinant of lateralization of expressive language in children with cerebral lesions.  Functional MRI language lateralization was attempted in 35 children (29 with epilepsy) aged 8 to 18 years with frontal or temporal lobe lesions (28 left hemisphere, 5 right hemisphere, and 2 bilateral).  Axial and coronal fMRI scans through the frontal and temporal lobes were acquired at 1.5 Tesla (T) by using a block-design, covert word-generation paradigm.  Activation maps were lateralized by blinded visual inspection and quantitative asymmetry indices (hemispheric and inferior frontal regions of interest, at p < 0.001 uncorrected and p < 0.05 Bonferroni corrected).  A total of 30 children showed significant activation in the inferior frontal gyrus.  Lateralization by visual inspection was left in 21, right in 6, and bilateral in 3, and concordant with hemispheric and inferior frontal quantitative lateralization in 93 % of cases.  Developmental tumors and dysplasias involving the inferior left frontal lobe had activation overlying or abutting the lesion in 5 of 6 cases.  Functional MRI language lateralization was corroborated in 6 children by frontal cortex stimulation or intra-carotid Amytal testing (IAT) and indirectly supported by aphasiology in a further 6 cases.  In 2 children, fMRI language lateralization was bilateral, and corroborative methods of language lateralization were left.  Neither lesion lateralization, patient handedness, nor developmental versus acquired nature of the lesion was associated with language lateralization.  Involvement of the left inferior or middle frontal gyri increased the likelihood of atypical language lateralization.  The authors concluded that this study suggests that fMRI lateralizes language in children with cerebral lesions.

Stancanello et al (2007) attempted to validate a method to exploit functional information for the identification of functional organs at risk (fOARs) in CyberKnife radiosurgery treatment planning.  Five patients affected by AVMs and scheduled to undergo radiosurgery were scanned prior to treatment using computed tomography (CT), three-dimensional rotational angiography (3D-RA), T2 weighted and blood oxygenation level dependent echo planar imaging MRI.  Tasks were chosen on the basis of lesion location by considering those areas which could be potentially close to treatment targets.  Functional data were superimposed on 3D-RA and CT used for treatment planning.  The procedure for the localization of fMRI areas was validated by direct ESM on 38 AVM and tumor patients undergoing conventional surgery.  Treatment plans studied with and without considering fOARs were significantly different, in particular with respect to both maximum dose and dose volume histograms; consideration of the fOARs allowed quality indices of treatment plans to remain almost constant or to improve in 4 out of 5 cases compared to plans with no consideration of fOARs.  The authors concluded that the presented method provides an accurate tool for the integration of functional information into AVM radiosurgery, which might help to minimize undesirable side effects and to make radiosurgery less invasive.

Stippich and colleagues (2007) prospectively evaluated the feasibility of standardized pre-surgical fMRI for localizing the Broca and Wernicke areas as well as for lateralizing language function.  A total of 81 patients (36 females, 45 males; aged 7 to 75 years) with different brain tumors underwent blood oxygen level-dependent fMRI at 1.5 T with two paradigms:
  1. sentence generation (SG), and
  2. word generation (WG). 
Functional MRI measurements, data processing, and evaluation were fully standardized by using dedicated software.  Four regions of interest were evaluated in each patient: the Broca and Wernicke areas and their anatomical homologs in the right hemisphere.  The SG and WG paradigms were successfully completed by all (100 %) and 70 (86 %) patients, respectively.  Success rates in localizing and lateralizing language were 96 % for the Broca and Wernicke areas with the SG paradigm, 81 % for the Broca area and 80 % for the Wernicke area with the WG paradigm, and 98 % for both areas when the SG and WG paradigms were used in combination.  Functional localizations were consistent for SG and WG paradigms in the inferior frontal gyrus (Broca area) and the superior temporal, supra-marginal, and angular gyri (Wernicke area).  Surgery was not performed in 7 patients (9 %) and was modified in 2 patients (2 %) because of fMRI findings.  The authors concluded that fMRI proved to be feasible during routine diagnostic neuro-imaging for localizing and lateralizing language function pre-operatively.

There is evidence for the use of fMRI in pre-surgical planning for epilepsy and monitoring of language function during tumor resection.

Roux et al (2003) analyzed the usefulness of pre-operative language fMRI by correlating fMRI data with intra-operative ESM results for patients with brain tumors.  Naming and verb generation tasks were used, separately or in combination, for 14 right-handed patients with tumors in the left hemisphere.  Acquired fMRI data were analyzed with statistical parametric mapping software, with two standard analysis thresholds (p < 0.005 and then p < 0.05). The fMRI data were then registered in a frameless stereotactic neuro-navigational device and correlated with direct brain mapping results.  These researchers used a statistical model with the fMRI information as a predictor, spatially correlating each intra-operatively mapped cortical site with fMRI data integrated in the neuro-navigational system (site-by-site correlation).  Eight patients were also studied with language fMRI post-operatively, with the same acquisition protocol.  These investigators observed high variability in signal extents and locations among patients with both tasks.  The activated areas were located mainly in the left hemisphere in the middle and inferior frontal gyri (F2 and F3), the superior and middle temporal gyri (T1 and T2), and the supra-marginal and angular gyri.  A total of 426 cortical sites were tested for each task among the 14 patients.  In frontal and temporo-parietal areas, poor sensitivity of the fMRI technique was observed for the naming and verb generation tasks (22 % and 36 %, respectively) with p < 0.005 as the analysis threshold.  Although not perfect, the specificity of the fMRI technique was good in all conditions (97 % for the naming task and 98 % for the verb generation task).  Better correlation (sensitivity, 59 %; specificity, 97 %) was achieved by combining the two fMRI tasks.  Variation of the analysis threshold to p < 0.05 increased the sensitivity to 66 % while decreasing the specificity to 91 %.  Post-operative fMRI data (for the cortical brain areas studied intra-operatively) were in accordance with brain mapping results for 6 of 8 patients.  Complete agreement between pre- and post-operative fMRI studies and direct brain mapping results was observed for only 3 of 8 patients.  The authors concluded that with the paradigms and analysis thresholds used in this study, language fMRI data obtained with naming or verb generation tasks, before and after surgery, were imperfectly correlated with intra-operative brain mapping results.  A better correlation could be obtained by combining the fMRI tasks.  The overall results of this study showed that language fMRI could not be used to make critical surgical decisions in the absence of direct brain mapping.  Other acquisition protocols are needed for evaluation of the potential role of language fMRI in the accurate detection of essential cortical language areas.

Benke and associates (2006) noted that recent studies have claimed that language fMRI can identify language lateralization in patients with TLE and that fMRI-based findings are highly concordant with the conventional assessment procedure of speech dominance, the IAT.  These researchers attempted to establish the power of language fMRI to detect language lateralization during pre-surgical assessment and compared the findings of a semantic decision paradigm with the results of a standard IAT in 68 patients with chronic intractable right and left TLE (rTLE, n = 28; lTLE, n = 40) who consecutively underwent a pre-surgical evaluation program.  The patient group also included 14 (20.6 %) subjects with atypical (bilateral or right hemisphere) speech.  Four raters used a visual analysis procedure to determine the laterality of speech-related activation individually for each patient.  Overall congruence between fMRI-based laterality and the laterality quotient of the IAT was 89.3 % in rTLE and 72.5 % in lTLE patients.  Concordance was best in rTLE patients with left speech.  In lTLE patients, language fMRI identified atypical, right hemisphere speech dominance in every case, but missed left hemisphere speech dominance in 17.2 %.  Frontal activations had higher concordance with the IAT than did activations in temporo-parietal or combined regions of interest.  Because of methodological problems, recognition of bilateral speech was difficult.  The authors concluded that these data provide evidence that language fMRI as used in the present study has limited correlation with the IAT, especially in patients with lTLE and with mixed speech dominance.  They noted that further refinements regarding the paradigms and analysis procedures will be needed to improve the contribution of language fMRI for pre-surgical assessment.

Petrella and colleagues (2006) prospectively evaluated the effect of pre-operative fMRI localization of language and motor areas on therapeutic decision making in patients with potentially resectable brain tumors.  A total of 39 consecutive patients (19 men, 20 women; mean age of 42.2 years) referred for fMRI for possible tumor resection were evaluated.  A pre-operative diagnosis of brain tumor was made in all patients.  Sentence completion and bilateral hand squeeze tasks were used to map language and sensorimotor areas.  Neurosurgeons completed questionnaires regarding the proposed treatment plan before and after fMRI and after surgery.  They also gave confidence ratings for fMRI results and estimated the effect on surgical time, extent of resection, and surgical approach.  The effect of fMRI on changes in treatment plan was assessed with the Wilcoxon signed rank test.  Differences in confidence ratings between altered and un-altered treatment plans were assessed with the Mann-Whitney U test.  The estimated influence of fMRI on surgical time, extent of resection, and surgical approach was denoted with summary statistics.  Treatment plans before and after fMRI differed in 19 patients (p < 0.05), with a more aggressive approach recommended after imaging in 18 patients.  There were no significant differences in confidence ratings for fMRI between altered and un-altered plans.  Functional MRI resulted in reduced surgical time (estimated reduction, 15 to 60 minutes) in 22 patients who underwent surgery, a more aggressive resection in 6, and a smaller craniotomy in 2.  The authors concluded that fMRI enables the selection of a more aggressive therapeutic approach than might otherwise be considered because of functional risk.  In certain patients, surgical time may be shortened, the extent of resection increased, and craniotomy size decreased. 

Di et al (2007) assessed the differences in brain activation in response to presentation of the patient's own name spoken by a familiar voice (SON-FV) in patients with vegetative state (VS) and minimally conscious state (MCS).  By using fMRI, these investigators prospectively studied residual cerebral activation to SON-FV in 7 patients with VS and 4 patients with MCS.  Behavioral evaluation was performed by means of standardized testing up to 3 months post-fMRI.  Two patients with VS failed to show any significant cerebral activation, while 3 patients with VS showed SON-FV induced activation within the primary auditory cortex.  Finally, 2 patients with VS and all 4 patients with MCS not only showed activation in primary auditory cortex but also in hierarchically higher order associative temporal areas.  The 2 patients with VS showing the most widespread activation subsequently showed clinical improvement to MCS observed 3 months after their fMRI scan.  The authors concluded that cerebral responses to patient's own name spoken by a familiar voice as measured by fMRI might be a useful tool to pre-clinically distinguish MCS-like cognitive processing in some patients behaviorally classified as vegetative. 

The American College of Radiology (ACR)'s guideline on neurological imaging for patients with epilepsy (Karis et al, 2006) noted that the data provided by MRI are essential in the pre-surgical evaluation of patients with medically refractory epilepsy, but noted that structurally detectable abnormalities are absent in many patients.  In these patients, functional studies provide useful information on localization of the seizure focus.  In this regard, functional imaging techniques, including positron emission tomography, single-photon emission computed tomography, magnetic source imaging, and fMRI, have contributed to the pre-surgical evaluation of patients with epilepsy.  The ACR guideline provided appropriateness ratings (1 = least appropriate; 9 = most appropriate) on fMRI for the following indications:

  • Chronic epilepsy, poor therapeutic response. Surgery candidate (rating = 5; may be helpful in pre-surgical planning).
  • New onset of seizure. Ethyl alcohol, and/or drug-related (rating = 2).
  • New onset seizure. Aged 18 to 40 years (rating = 2).
  • New onset seizure. Aged greater than 40 years (rating = 2).
  • New onset seizure. Focal neurological deficit (rating = 2).

Additionally, the ACR's guideline on neurological imaging for patients with head trauma (Davis et al, 2006) provided an appropriateness rating of 2 for patients with sub-acute or chronic closed head injury with cognitive and/or neurological deficit(s).

The Ontario Ministry of Health and Long-Term Care's review on functioning brain imaging (2006) stated that there may be a role for fMRI in the identification of surgical candidates for tumor resection.  The review also stated that there may be some clinical utility for fMRI in pre-surgical functional mapping.  

The assessment by the Ontario Ministry of Health and Long-Term Care (2006) stated that there is limited clinical utility of functional brain imaging in the management of patients with MS at this time.  This is in agreement with the European Federation of Neurological Societies' guideline on the use of neuro-imaging in the management of MS (Filippi et al, 2006), which stated that the use of non-conventional MRI techniques (e.g., fMRI, diffusion tensor MRI, magnetization transfer MRI, and MR spectroscopy) is not recommended.

Rocca and colleagues (2008) used fMRI to examine the properties of the mirror neuron system (MNS) in patients with MS.  Using a 3 tesla scanner, these researchers acquired fMRI in 16 right-handed patients with relapsing-remitting MS and 14 controls.  Two motor tasks were studied.  The first consisted of repetitive flexion-extension of the last 4 fingers of the right hand (simple task) alternated to epochs of rest; the second (MNS task) consisted of observation of a movie showing the hand of another subject while performing the same task.  During the simple task, compared to controls, patients with MS had more significant activations of the contralateral primary sensori-motor cortex and supplementary motor area.  During the MNS task, both groups showed the activation of several visual areas, the infra-parietal sulcus, and the inferior frontal gyrus (IFG), bilaterally.  The IFG and the visual areas were significantly more active in patients than controls.  The between-group interaction analysis showed that in patients with MS, part of the regions of the MNS were more active also during the simple task.  The authors concluded that the findings of this study suggested increased activation of the MNS in patients with MS with a normal level of function and widespread damage of the central nervous system.  The potentialities of this system in facilitating clinical recovery in patients with MS and other neurological conditions should be investigated.

In an editorial that accompanied the afore-mentioned article, Phillips (2008) stated that fMRI has tremendous potential for assessing and better understanding MS.  He noted that it is important to remember that fMRI is an indirect measurement of neuronal activity.  Also, it has been reported that there is altered brain perfusion in patients with MS.  Changes in perfusion may alter the sensitivity and statistical characteristics of fMRI.  Currently, it is unclear to what extent altered tissue perfusion complicates the interpretation of fMRI in MS.  Furthermore, the enhanced activation patterns observed in MS have also been shown in other neurological conditions such as Alzheimer's disease, Parkinson's disease, and stroke.

In a randomized, double-blind, placebo-controlled study, Atri et al (2011) examined the feasibility and test-retest reliability of encoding-task fMRI in mild Alzheimer disease (AD).  These investigators studied 12 patients with mild AD (mean [SEM] Mini-Mental State Examination score, 24.0 [0.7]; mean Clinical Dementia Rating score, 1.0) who had been taking donepezil hydrochloride for more than 6 months from the placebo-arm of a larger 24-week study (n = 24, 4 scans on weeks 0, 6, 12, and 24, respectively).  They performed whole-brain t maps (p < 0.001, 5 contiguous voxels) and hippocampal regions-of-interest analyses of extent (percentage of active voxels) and magnitude (percentage of signal change) for novel-greater-than-repeated face-name contrasts.  These researchers also calculated intra-class correlation coefficients and power estimates for hippocampal regions of interest.  Task tolerability and data yield were high (95 of 96 scans yielded favorable-quality data).  Whole-brain maps were stable.  Right and left hippocampal regions-of-interest intraclass correlation coefficients were 0.59 to 0.87 and 0.67 to 0.74, respectively.  To detect 25.0 % to 50.0 % changes in week-0 to week-12 hippocampal activity using left-right extent or right magnitude with 80.0 % power (2-sided α = 0.05) requires 14 to 51 patients.  Using left magnitude requires 125 patients because of relatively small signal to variance ratios.  The authors concluded that encoding-task fMRI was successfully implemented in a single-site, 24-week, AD randomized controlled trial.  Week 0 to 12 whole-brain t maps were stable, and test-retest reliability of hippocampal fMRI measures ranged from moderate to substantial.  Right hippocampal magnitude may be the most promising of these candidate measures in a leveraged context.  These initial estimates of test-retest reliability and power justify evaluation of encoding-task fMRI as a potential biomarker for signal of effect in exploratory and proof-of-concept trials in mild AD.  They stated that validation of these results with larger sample sizes and assessment in multi-site studies is warranted.

Burgmer and colleagues (2010) stated that studies with functional neuroimaging support the hypothesis of central pain augmentation in fibromyalgia syndrome (FMS) with functional differences in areas of the medial pain system.  These investigators examined if these findings are unique to patients with FMS.  BOLD-signal patterns during and before tonic experimental pain were compared to healthy controls and patients with rheumatoid arthritis (RA) as a chronic pain disorder of somatic origin. These researchers expected different BOLD-signal patterns in areas of the medial pain system that were most pronounced in patients with FMS.  An fMRI-block design before, during and after an incision was performed in patients with FMS (n = 17), RA (n = 16) and in healthy controls (n = 17).  A 2-factorial model of BOLD-signal changes was designed to explore significant differences of brain activation between the groups during the pain stimulus.  Additionally, the correlation of brain activity during the anticipation of pain with the amount of the impending pain was determined.  These researchers observed a FMS-unique temporal brain activation of the frontal cortex in patients with FMS.  Moreover, areas of the motor cortex and the cingulate cortex presented a FMS-specific relation between brain activity during pain anticipation and the magnitude of the subsequent pain experience.  The authors concluded that these findings support the hypothesis that central mechanisms of pain processing in the frontal cortex and cingulate cortex may play an important role in patients with FMS.

Tregellas et al (2010) noted that 3-(2,4-Dimethoxybenzylidene)-anabaseine (DMXB-A) is a partial agonist at alpha7-nicotinic acetylcholine receptors and is now in early clinical development for treatment of deficits in neurocognition and sensory gating in schizophrenia.  During its initial phase II test, fMRI studies were conducted to determine whether the drug had its intended effect on hippocampal inhibitory interneurons.  Increased hemodynamic activity in the hippocampus in schizophrenia is found during many tasks, including smooth pursuit eye movements, and may reflect inhibitory dysfunction.  Placebo and 2 doses of drug were administered in a random, double-blind cross-over design.  After the morning drug/placebo ingestion, subjects underwent fMRI while performing a smooth pursuit eye movement task.  Data were analyzed from 16 non-smoking patients, including 7 women and 9 men.  The 150-mg dose of DMXB-A, compared with placebo, diminished the activity of the hippocampus during pursuit eye movements, but the 75-mg dose was ineffective.  The effect at the 150-mg dose was negatively correlated with plasma drug levels.  The findings are consistent with the previously established function of alpha7-nicotinic receptors on inhibitory interneurons in the hippocampus and with genetic evidence for deficits in these receptors in schizophrenia.  Imaging of drug response is useful in planning future clinical tests of this compound and other nicotinic agonists for schizophrenia.

Whalley et al (2012) noted that although bipolar disorder (BD) and schizophrenia (SCZ) have a number of clinical features and certain susceptibility genes in common, they are considered separate disorders, and it is unclear which aspects of pathophysiology are specific to each condition.  These researchers examined the fMRI literature to determine the evidence for diagnosis-specific patterns of brain activation in these 2 patient groups.  A systematic search was performed to identify fMRI studies directly comparing BD and SCZ to examine evidence for diagnosis-specific activation patterns.  Studies were categorized into
  1. those investigating emotion, reward, or memory,
  2. those describing executive function or language tasks, and
  3. those looking at the resting state or default mode networks. 
Studies reporting estimates of sensitivity and specificity of classification were also summarized, followed by studies reporting associations with symptom severity measures.  A total of 21 studies were identified including patients (n = 729) and healthy subjects (n = 465).  Relative over-activation in the medial temporal lobe and associated structures was found in BD versus SCZ in tasks involving emotion or memory.  Evidence of differences between the disorders in pre-frontal regions was less consistent.  Accuracy values for assignment of diagnosis were generally lower in BD than in SCZ.  Few studies reported significant symptom associations; however, these generally implicated limbic regions in association with manic symptoms.  The authors concluded that although there are a limited number of studies and a cautious approach is warranted, activation differences were found in the medial temporal lobe and associated limbic regions, suggesting the presence of differences in the neurobiological substrates of SCZ and BD.  They stated that future studies examining symptom dimensions, risk-associated genes, and the effects of medication will aid clarification of the mechanisms behind these differences.

Astrakas et al (2012) stated that the number of individuals suffering from stroke is increasing daily, and its consequences are a major contributor to invalidity in today's society.  Stroke rehabilitation is relatively new, having been hampered from the long-standing view that lost functions were not recoverable.  Nowadays, robotic devices, which aid by stimulating brain plasticity, can assist in restoring movement compromised by stroke-induced pathological changes in the brain that can be monitored by MRI.  Multi-parametric MRI of stroke patients participating in a training program with a novel Magnetic Resonance Compatible Hand-Induced Robotic Device (MR_CHIROD) could yield a promising biomarker that, ultimately, will enhance the ability to advance hand motor recovery following chronic stroke.  Using state-of-the art MRI in conjunction with MR_CHIROD-assisted therapy can provide novel biomarkers for stroke patient rehabilitation extracted by a meta-analysis of data.  Successful completion of such studies may provide a ground breaking method for the future evaluation of stroke rehabilitation therapies.  Their results will attest to the effectiveness of using MR-compatible hand devices with MRI to provide accurate monitoring during rehabilitative therapy.  Furthermore, such results may identify biomarkers of brain plasticity that can be monitored during stroke patient rehabilitation.  The potential benefit for chronic stroke patients is that rehabilitation may become possible for a longer period of time after stroke than previously thought, unveiling motor skill improvements possible even after 6 months due to retained brain plasticity.

Wager et al (2013) noted that persistent pain is measured by means of self-report, the sole reliance on which hampers diagnosis and treatment.  Functional magnetic resonance imaging holds promise for identifying objective measures of pain, but brain measures that are sensitive and specific to physical pain have not yet been identified.  In 4 studies involving a total of 114 participants, these researchers developed an fMRI-based measure that predicts pain intensity at the level of the individual person.  In study 1, they used machine-learning analyses to identify a pattern of fMRI activity across brain regions -- a neurologic signature -- that was associated with heat-induced pain.  The pattern included the thalamus, the posterior and anterior insulae, the secondary somatosensory cortex, the anterior cingulate cortex, the peri-aqueductal gray matter, and other regions.  In study 2, these investigators tested the sensitivity and specificity of the signature to pain versus warmth in a new sample.  In study 3, they assessed specificity relative to social pain, which activates many of the same brain regions as physical pain.  In study 4, these researchers evaluated the responsiveness of the measure to the analgesic agent remifentanil.  In study 1, the neurologic signature showed sensitivity and specificity of 94 % or more (95 % confidence interval [CI]: 89 to 98) in discriminating painful heat from non-painful warmth, pain anticipation, and pain recall.  In study 2, the signature discriminated between painful heat and non-painful warmth with 93 % sensitivity and specificity (95 % CI: 84 to 100).  In study 3, it discriminated between physical pain and social pain with 85 % sensitivity (95 % CI: 76 to 94) and 73 % specificity (95 % CI, 61 to 84) and with 95 % sensitivity and specificity in a forced-choice test of which of 2 conditions was more painful.  In study 4, the strength of the signature response was substantially reduced when remifentanil was administered.  The authors concluded that it is possible to use fMRI to assess pain elicited by noxious heat in healthy persons.  Moreover, they state that future studies are needed to assess whether the signature predicts clinical pain.

Magland and Childress (2014) stated that real-time fMRI is especially vulnerable to task-correlated movement artifacts because statistical methods normally available in conventional analyses to remove such signals cannot be used in the context of real-time fMRI.  Multi-voxel classifier-based methods, although advantageous in many respects, are particularly sensitive.  These researchers systematically studied various movements of the head and face to determine to what extent these can "masquerade" as signal in multi-voxel classifiers.  A total of 10 subjects were instructed to move systematically (12 instructed movements) throughout fMRI exams and data from a previously published real-time study was also analyzed to determine the extent to which non-neural signals contributed to the high reported accuracy in classifier output.  Of potential concern, whole-brain classifiers based solely on movements exhibited false positives in all cases (p < 0.05).  Artifacts were also observed in the spatial activation maps for 2 of the 12 movement tasks.  In the retrospective analysis, it was determined that the relatively high reported classification accuracies were (fortunately) mostly explainable by neural activity, but that in some cases performance was likely dominated by movements.  The authors concluded that movement tasks of many types (including movements of the body, eyes, and face) can lead to false positives in classifier-based real-time fMRI paradigms.

The University of Michigan Health System’s clinical guideline on “Attention-deficit hyperactivity disorder” (2013) listed functional magnetic resonance imaging as one of the search terms for the update of a previous version of this guideline.  Moreover, the updated guideline stated that “Diagnosis is based on the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV-TR) criteria.  The three main types are primary hyperactive, primary inattentive, and combined.  No specific test can make the diagnosis”.

Furthermore, UpToDate reviews on “Attention deficit hyperactivity disorder in children and adolescents: Clinical features and evaluation” (Krull, 2014) and “Adult attention deficit hyperactivity disorder in adults: Epidemiology, pathogenesis, clinical features, course, assessment, and diagnosis” (Bukstein, 2014) do not mention the use of fMRI as a diagnostic tool.

Wager and colleagues (2013) noted that persistent pain is measured by means of self-report, the sole reliance on which hampers diagnosis and treatment.  Functional MRI holds promise for identifying objective measures of pain, but brain measures that are sensitive and specific to physical pain have not yet been identified.  In 4 studies involving a total of 114 participants, these researchers developed an fMRI-based measure that predicts pain intensity at the level of the individual person.  In study 1, they used machine-learning analyses to identify a pattern of fMRI activity across brain regions -- a neurologic signature -- that was associated with heat-induced pain.  The pattern included the thalamus, the posterior and anterior insulae, the secondary somatosensory cortex, the anterior cingulate cortex, the periaqueductal gray matter, and other regions.  In study 2, these researchers tested the sensitivity and specificity of the signature to pain versus warmth in a new sample.  In study 3, they assessed specificity relative to social pain, which activates many of the same brain regions as physical pain.  In study 4, these investigators assessed the responsiveness of the measure to the analgesic agent remifentanil.  In study 1, the neurologic signature showed sensitivity and specificity of 94 % or more (95 % CI: 89 to 98) in discriminating painful heat from non-painful warmth, pain anticipation, and pain recall.  In study 2, the signature discriminated between painful heat and non-painful warmth with 93 % sensitivity and specificity (95 % CI: 84 to 100).  In study 3, it discriminated between physical pain and social pain with 85 % sensitivity (95 % CI: 76 to 94) and 73 % specificity (95 % CI: 61 to 84) and with 95 % sensitivity and specificity in a forced-choice test of which of 2 conditions was more painful.  In study 4, the strength of the signature response was substantially reduced when remifentanil was administered.  The authors concluded that it is possible to use fMRI to assess pain elicited by noxious heat in healthy persons.  Moreover, they stated that future studies are needed to assess whether the signature predicts clinical pain.

Furthermore, the Work Loss Data Institute’s guideline on “Pain (chronic)” (2013) listed fMRI as one of the interventions that were considered, but are not recommended.

Stender et al (2014) stated that bedside clinical examinations can have high rates of misdiagnosis of unresponsive wakefulness syndrome (vegetative state) or minimally conscious state. The diagnostic and prognostic usefulness of neuroimaging-based approaches has not been established in a clinical setting. These researchers carried out a validation study of 2 neuroimaging-based diagnostic methods:
  1. PET imaging and
  2. functional MRI (fMRI).
For this clinical validation study, these investigators included patients referred to the University Hospital of Liege, Belgium, between January, 2008, and June, 2012, who were diagnosed by the authors’ unit with unresponsive wakefulness syndrome, locked-in syndrome, or minimally conscious state with traumatic or non-traumatic causes. They did repeated standardized clinical assessments with the Coma Recovery Scale-Revised (CRS-R), cerebral (18)F-fluorodeoxyglucose (FDG) PET, and fMRI during mental activation tasks. They calculated the diagnostic accuracy of both imaging methods with CRS-R diagnosis as reference. They assessed outcome after 12 months with the Glasgow Outcome Scale-Extended. The authors included 41 patients with unresponsive wakefulness syndrome, 4 with locked-in syndrome, and 81 in a minimally conscious state (48 = traumatic, 78 = non-traumatic; 110 = chronic, 16 = subacute). (18)F-FDG PET had high sensitivity for identification of patients in a minimally conscious state (93 %, 95 % CI: 85 to 98) and high congruence (85 %, 77 to 90 %) with behavioral CRS-R scores. The active fMRI method was less sensitive at diagnosis of a minimally conscious state (45 %, 30 to 61 %) and had lower overall congruence with behavioral scores (63 %, 51 to 73 %) than PET imaging. (18)F-FDG PET correctly predicted outcome in 75 of 102 patients (74 %, 64 to 81 %), and fMRI in 36 of 65 patients (56 %, 43 to 67 %); 13 of 41 (32 %) of the behaviorally unresponsive patients (i.e., diagnosed as unresponsive with CRS-R) showed brain activity compatible with (minimal) consciousness (i.e., activity associated with consciousness, but diminished compared with fully conscious individuals) on at least 1 neuroimaging test; 69 % of these (9 of 13) patients subsequently recovered consciousness. The authors concluded that cerebral (18)F-FDG PET could be used to complement bedside examinations and predict long-term recovery of patients with unresponsive wakefulness syndrome. Moreover, they stated that active fMRI might also be useful for differential diagnosis, but seems to be less accurate.

UpToDate reviews on “Locked-in syndrome’ (Caplan, 2015) and “Stupor and coma in adults” (Young, 2015) do not mention functional MRI as a management tool.

Furthermore, an UpToDate review on “Treatment and prognosis of coma in children’ (Thompson and Williams, 2015) states that “Other neuroimaging modalities, MR spectroscopy, functional MRI, positron emission tomography are not useful in the evaluation of coma prognosis]. Studies, awaiting validation, suggest that these tools may help discriminate between persistent vegetative state, minimally conscious state and other states of impaired consciousness”.

Anoxic-Ischemic Brain Injury

An UpToDate review on “Hypoxic-ischemic brain injury: Evaluation and prognosis” (Weinhouse and Young, 2016) states that “In the future, larger studies may find a role for standard MRI as well as functional neuroimaging, such as positron emission tomography (PET) and functional MRI (fMRI), in the prognostic assessment of adults with anoxic-ischemic brain injury …. fMRI studies have the potential to detect network processing of sensory and motor responses, showing some evidence of awareness in a small proportion of behaviorally unresponsive patients.  However, the performance and interpretation of these studies remains complex and is still investigational.  There are also ethical issues regarding quality of life in decision-making that need to be resolved, namely whether patients who can generate such binary responses can participate in a decision-making process”.

Psychotic Depression

O'Connor and Agius (2015) stated that psychotic depression is widely accepted as a specific subtype of unipolar major depression.  Magnetic resonance imaging studies have begun to investigate the neurobiological changes that differentiate this subtype of major depression from non-psychotic depression.  Any differences may eventually be useful in aiding diagnosis patients for whom there is diagnostic uncertainty.  This review collated the currently available evidence.  These investigators performed a systematic search of the Medline, PubMed, Embase & Web of Science databases was used to identify all articles comparing structural grey matter or fMRI differences between adults (18 years or older) with previously diagnosed psychotic and non-psychotic depression in pre-defined regions of interest (hippocampus, amygdala, cingulate, insula and frontal cortices).  The results were collated and organized according to brain region.  There was a paucity of studies addressing structural and functional changes differentiating these 2 disorders and recommendations regarding use of these modalities in diagnosis cannot be made.  From the available studies decreases in frontal cortex grey matter volumes may differentiate psychotic from non-psychotic depression while further studies are needed to confirm decreases in insula cortex volumes.  Functional MRI studies showed associations between altered activity in these 2 regions and cognitive impairments in patients with psychotic depression.  The volumes of putative emotional processing regions including the amygdala, hippocampus and anterior cingulate showed no difference between psychotic and non-psychotic depression.  The authors concluded that structural and functional changes in the higher associative regions of the frontal and insular cortices appeared to differentiate psychotic and non-psychotic depression to a greater degree than changes in putative emotional processing regions.  The quality of the evidence both in terms of numbers of studies available and sample sizes involved was very poor; but in regard to directing future study, understanding the neurobiology of psychotic depression may benefit from a more detailed assessment of these 2 regions.

Temporal Neocortical Epilepsy and Temporal Tumors

On behalf of the American Academy of Neurology (AAN), Szaflarski and colleagues (2017) evaluated the diagnostic accuracy and prognostic value of fMRI in determining lateralization and predicting post-surgical language and memory outcomes.  An 11-member panel rated available evidence according to the 2004 AAN process.  At least 2 panelists reviewed the full text of 172 articles and selected 37 for data extraction.  Case reports, reports with less than 15 cases, meta-analyses, and editorials were excluded.  The authors concluded that the use of fMRI may be considered an option for lateralizing language functions in place of intracarotid amobarbital procedure (IAP) in patients with medial temporal lobe epilepsy (MTLE; Level C), temporal epilepsy in general (Level C), or extra-temporal epilepsy (Level C).  For patients with temporal neocortical epilepsy or temporal tumors, the evidence is insufficient (Level U).  They stated that fMRI may be considered to predict post-surgical language deficits after anterior temporal lobe resection (Level C).  The use of fMRI may be considered for lateralizing memory functions in place of IAP in patients with MTLE (Level C), but is of unclear utility in other epilepsy types (Level U).  Moreover, they stated that fMRI of verbal memory or language encoding should be considered for predicting verbal memory outcome (Level B); and fMRI using non-verbal memory encoding may be considered for predicting visuospatial memory outcomes (Level C).  These investigators noted that pre-surgical fMRI could be an adequate alternative to IAP memory testing for predicting verbal memory outcome (Level C).

Anxiety Disorder

Wang and colleagues (2018) noted that impairments in emotion regulation, and more specifically in cognitive re-appraisal, are thought to play a key role in the pathogenesis of anxiety disorders.  However, the available evidence on such deficits is inconsistent.  To further illustrate the neurobiological underpinnings of anxiety disorder, the present meta-analysis summarized fMRI findings for cognitive re-appraisal tasks and investigated related brain areas.  These investigators performed a comprehensive series of meta-analyses of cognitive reappraisal fMRI studies contrasting patients with anxiety disorder with healthy control (HC) subjects, employing an anisotropic effect-size signed differential mapping approach.  They also conducted a subgroup analysis of medication status, anxiety disorder subtype, data-processing software, and MRI field strengths.  Meta-regression was used to explore the effects of demographics and clinical characteristics.  A total of 8 studies, with 11 datasets including 219 patients with anxiety disorder and 227 HC, were identified.  Compared with HC, patients with anxiety disorder showed relatively decreased activation of the bilateral dorsomedial prefrontal cortex (dmPFC), bilateral dorsal anterior cingulate cortex (dACC), bilateral supplementary motor area (SMA), left ventromedial prefrontal cortex (vmPFC), bilateral parietal cortex, and left fusiform gyrus during cognitive re-appraisal.  The subgroup analysis, jackknife sensitivity analysis, heterogeneity analysis, and Egger's tests further confirmed these findings.  The authors concluded that they identified the most robust functional neuroimaging findings on cognitive re-appraisal in anxiety disorder.  The results demonstrated that patients with anxiety disorder could not recruit the prefronto-parietal network, including the dmPFC, dACC, SMA, vmPFC, and parietal cortex, to down-regulate their emotion response.  These findings provided robust evidence that impairment of prefronto-parietal neuronal circuits may play an important role in the pathogenesis of anxiety disorder.  This finding may provide novel targets for medical or cognitive-behavioral interventions and neuromodulation approaches (e.g., transcranial magnetic stimulation).  These researchers stated that with longitudinal data, future investigations should further explore whether these functional abnormities are associated with structural changes or influenced by disease severity and medication status.

The authors stated that this study had several drawbacks.  First, the number of fMRI studies included was small; the literature search yielded only 8 studies with 11 relevant patients versus controls comparisons.  This could affect the generalizability of these findings, particularly in the subgroup meta-analyses and meta-regressions analyses.  Second, this meta-analysis was based on co-ordinates from published studies rather than raw statistical maps, which might reduce its accuracy.  Third, the heterogeneity of the data acquisition and analysis techniques, including MRI field strengths, slice thickness, voxel size, and data-processing software, may reduce the accuracy of these results.  Fourth, this meta-analysis included studies of medicine-naive patients who had undergone a medication wash-out period before scanning, so that longer-term influences of medication on brain function could not be completely excluded.  Although these researchers conducted a subgroup meta-analysis of the medicine-naive, these results should be interpreted with caution.  Finally, some patients with anxiety disorder had co-morbid major depression.  Anxiety and major depressive disorders may have different disorder-specific deficits in the neural mechanisms of cognitive re-appraisal.  Although the patients fulfilled their criteria for co-morbid major depression with anxiety disorder being the primary diagnosis, the influence of major depression could not be completely ruled out.

Childhood Mal-Treatment

Heany and associates (2018) stated that childhood mal-treatment, including abuse and neglect, may have sustained effects on the integrity and functioning of the brain, alter neurophysiological responsivity later in life, and pre-dispose individuals toward psychiatric conditions involving socio-affective disturbances.  This meta-analysis quantified associations between self-reported childhood mal-treatment and brain function in response to socio-affective cues in adults.  A total of 17 fMRI studies reporting on data from 848 individuals examined with the Childhood Trauma Questionnaire were included in a meta-analysis of whole-brain findings, or a review of region of interest findings.  The spatial consistency of peak activations associated with mal-treatment exposure was tested using activation likelihood estimation, using a threshold of p < 0.05 corrected for multiple comparisons.  Adults exposed to childhood mal-treatment showed significantly increased activation in the left superior frontal gyrus and left middle temporal gyrus, and decreased activation in the left superior parietal lobule and the left hippocampus.  Although hyper-responsivity to socio-affective cues in the amygdala and ventral anterior cingulate cortex in correlation with mal-treatment severity was a replicated finding in region of interest studies, null results were reported as well.  The authors concluded that these findings suggested that childhood mal-treatment had sustained effects on brain function into adulthood, and high-lighted potential mechanisms for conveying vulnerability to development of psychopathology.  These findings need to be further investigated.

Obsessive-Compulsive Disorder

Lu and co-workers (2020) presented a study protocol for a single-blind, randomized controlled trial (RCT) to examine the feasibility and efficacy of mindfulness-based cognitive therapy.  A total of 120 un-medicated Chinese obsessive-compulsive disorder (OCD) patients will be randomized to the mindfulness-based cognitive therapy group, the selective serotonin reuptake inhibitor group or the psycho-education group for 11 sessions in 10 weeks.  A range of scales for clinical symptoms and fMRI will be completed at baseline (week 0), mid-intervention (week 4), post-intervention (week 10) and the 6-month follow-up (weeks 14, 22 and 34).  The authors stated that this study will have relevance to decisions about therapeutic options for un-medicated OCD patients.

Panic Disorder

Ni and colleagues (2020) stated that panic disorder (PD) is a prevalent anxiety disorder, however, its neurobiology remains poorly understood.  It has been proposed that the pathophysiology of PD is related to an abnormality in a particular neural network.  However, most studies investigating resting-state functional connectivity (FC) have relied on a priori restrictions of seed regions, which may bias observations.  These investigators examined changes in intra- and inter-network FC in the whole brain of patients with PD using resting-state fMRI.  A voxel-wise data-driven independent component analysis was performed on 26 PD patients and 27 healthy controls (HCs).  They compared the differences in the intra- and inter-network FC between the 2 groups of subjects using statistical parametric mapping with 2-sample t-tests.  PD patients exhibited decreased intra-network FC in the right anterior cingulate cortex (ACC) of the anterior default mode network, the left pre-central and post-central gyrus of the sensori-motor network, the right lobule V/VI, the cerebellum vermis, and the left lobule VI of the cerebellum network compared with the HCs.  The intra-network FC in the right ACC was negatively correlated with symptom severity.  None of the pairs of resting state networks showed significant differences in functional network connectivity between the 2 groups.  The authors concluded that these results suggested that the brain networks associated with emotion regulation, interoceptive awareness, and fear and somatosensory processing may play an important role in the pathophysiology of PD. 

Furthermore, an UpToDate review on “Panic disorder in adults: Epidemiology, pathogenesis, clinical manifestations, course, assessment, and diagnosis” (Roy-Byrne, 2020) does not mention functional MRI as a management option.

Psychosis

Gonzalez-Vivas and colleagues (2019) noted that little is known regarding changes in brain functioning after 1st-episode psychosis (FEP).  Such knowledge is important for predicting the course of disease and adapting interventions. and fMRI has become a promising tool for examining brain function at the time of symptom onset and at follow-up.  These researchers carried out a systematic review of longitudinal fMRI studies with FEP patients according to PRISMA guidelines.  Resting-state and task-activated studies were considered together.  A total of 11 studies were included; they reported on a total of 236 FEP patients who were evaluated by 2 fMRI scans and clinical assessments; 5 studies found hypo-activation at baseline in prefrontal cortex areas, 2 studies found hypo-activation in the amygdala and hippocampus, and 3 others found hypo-activation in the basal ganglia.  Other hypo-activated areas were the anterior cingulate cortex, thalamus and posterior cingulate cortex; 10 out of 11 studies reported (partial) normalization by increased activation after anti-psychotic treatment.  A minority of studies observed hyper-activation at baseline.  The authors concluded that this review of longitudinal FEP samples studies showed a pattern of predominantly hypo-activation in several brain areas at baseline that may normalize to a certain extent following treatment.  These investigators stated that these findings should be interpreted with caution given the small number of studies and their methodological and clinical heterogeneity.

Language and Memory Decline (Evaluation After Epilepsy Surgery)

Schmid and colleagues (2018) noted that the European Union-funded E-PILEPSY project was launched to develop guidelines and recommendations for epilepsy surgery.  In a systematic review, these investigators evaluated the diagnostic accuracy of fMRI, Wada test, magnetoencephalography (MEG), and functional transcranial Doppler sonography (fTCD) for language and memory decline after surgery.  They carried out a literature search using PubMed, Embase, and CENTRAL.  The diagnostic accuracy was expressed in terms of sensitivity and specificity for post-operative language or memory decline, as determined by pre- and post-operative neuropsychological assessments.  If 2 or more estimates of sensitivity or specificity were extracted from a study, 2 meta-analyses were conducted, using the maximum ("best case") and the minimum ("worst case") of the extracted estimates, respectively.  A total of 28 papers were eligible for data extraction and further analysis.  All tests for heterogeneity were highly significant, indicating large between-study variability (p < 0.001).  For memory outcomes, meta-analyses were conducted for Wada tests (n = 17) using both memory and language laterality quotients.  In the best case, meta-analyses yielded a sensitivity estimate of 0.79 (95 % CI: 0.67 to 0.92) and a specificity estimate of 0.65 (95 % CI: 0.47 to 0.83).  For the worst case, meta-analyses yielded a sensitivity estimate of 0.65 (95 % CI: 0.48 to 0.82) and a specificity estimate of 0.46 (95 % CI: 0.28 to 0.65).  The overall quality of evidence, which was assessed using Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology, was rated as very low.  Meta-analyses concerning diagnostic accuracy of fMRI, fTCD, and MEG were not feasible due to small numbers of studies (fMRI, n = 4; fTCD, n = 1; MEG, n = 0).  This also applied to studies concerning language outcomes (Wada test, n = 6; fMRI, n = 2; fTCD, n = 1; MEG, n = 0).  The authors concluded that meta-analyses could only be conducted in a few subgroups for the Wada test with low-quality evidence.  Thus, more evidence from high-quality studies and improved data reporting are needed.  Moreover, these researchers stated that the large between-study heterogeneity underlined the necessity for more homogeneous and thus comparable studies in future research.

Sleep Behavior Disorder

Campabadal and colleagues (2021) noted that isolated rapid eye movement sleep behavior disorder (iRBD) is a harbinger for developing clinical synucleinopathies; and MRI has been suggested as a tool for understanding the neural bases of iRBD and its evolution.  In a systematic review, these researchers analyzed original full text articles on structural MRI as well as fMRI in patients with video-polysomnography-confirmed iRBD according to systematic procedures suggested by Reviews and Meta-analyses (PRISMA).  The literature search was performed via the PubMed database for articles related to structural MRI and fMRI in iRBD from 2000 to 2020.  Investigations to-date have been diverse in terms of methodology, but most agree that patients with iRBD have structural changes in deep gray matter nuclei, cortical gray matter atrophy, and disrupted functional connectivity within the basal ganglia, the cortico-striatal and cortico-cortical networks.  In addition, there is evidence that MRI detects structural and functional brain alterations associated with the motor and non-motor symptoms of iRBD.  The authors concluded that this review highlighted the need for larger, multi-center, longitudinal studies that will aid in identifying structural and functional patterns of brain degeneration.  These researchers stated that it is anticipated that MRI may soon be able to help with the monitoring of disease progression, and perhaps even be of use in determining the short-term risk of subsequent phenoconversion.  Finally, larger case-controlled further research must study iRBD longitudinally, implementing multi-modal imaging that uses complex approaches based on data-driven and unsupervised machine learning.  This should provide greater insights into the natural course of iRBD.

The authors stated that the main drawback of this study was its small sample size of 10 patients with iRBD and the persistent controversy over the use of T1-weighted images to study structural connectivity.  Some investigators argued that gray matter structural connectivity could not be considered a direct measure of connectivity, unlike the diffusion tensor imaging (DTI) approach, while others considered that this approach provided additional insights into the brain network topographical organization.  Given evidence that white matter changes could be demonstrated with DTI approaches and that structural connectivity research is possible via diffusion MRI tractography, this is a promising field that requires further investigation with larger iRBD cohorts.

Anger and Aggressive Behaviors

Nikolic et al (2022) stated that reactive aggression in response to perceived threat or provocation is part of humans' adaptive behavioral repertoire; however, high levels of aggression can result in the violation of social and legal norms.  Understanding brain function in individuals with high levels of aggression as they process anger- and aggression-eliciting stimuli is critical for refining explanatory models of aggression; thus, improving interventions.  These researchers noted that 3 neurobiological models of reactive aggression -- the limbic hyperactivity, prefrontal hypoactivity, and dysregulated limbic-prefrontal connectivity models -- have been proposed.  However, these models were based on neuroimaging studies involving mainly non-aggressive individuals, leaving it unclear which model best described brain function in those with a history of aggression.  These investigators carried out a systematic literature search (PubMed and Psycinfo) and Multilevel Kernel Density meta-analysis (MKDA) of 9 fMRI studies (8 included in the between-group analysis [i.e., aggression versus control groups], 5 in the within-group analysis).  Studies examined brain responses to tasks putatively eliciting anger and aggression in individuals with a history of aggression alone and relative to controls.  Individuals with a history of aggression exhibited greater activity in the superior temporal gyrus and in regions comprising the cognitive control and default mode networks (right posterior cingulate cortex, precentral gyrus, precuneus, right inferior frontal gyrus) during reactive aggression relative to baseline conditions.  Compared to controls, individuals with a history of aggression exhibited increased activity in limbic regions (left hippocampus, left amygdala, left para-hippocampal gyrus) and temporal regions (superior, middle, inferior temporal gyrus), and reduced activity in occipital regions (left occipital cortex, left calcarine cortex).  The authors concluded that the findings of this study lend support to the limbic hyperactivity model in individuals with a history of aggression, and further indicated altered temporal and occipital activity in anger- and aggression-eliciting conditions involving face and speech processing.  The clinical implications of these findings need to be further investigated in well-designed studies.

Autism Spectrum Disorder

Santana et al (2022) noted that the diagnosis of autism spectrum disorder (ASD) is still based on behavioral criteria via a lengthy and time-consuming process.  Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis.  In particular, using Machine Learning classifiers based on resting-state fMRI (rs-fMRI) data is promising; however, there is an ongoing need for further research on their accuracy and reliability.  In a systematic review and meta-analysis, these researchers examined the available evidence in the literature so far.  They employed a bi-variate random-effects meta-analytic model to examine the sensitivity and specificity across the 55 studies that offered sufficient information for quantitative analysis.  The findings indicated overall summary sensitivity and specificity estimates of 73.8 % and 74.8 %, respectively.  Support Vector Machine (SVM) stood out as the most used classifier, presenting summary estimates above 76 %.  Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for Artificial Neural Network (ANN) classifiers.  The use of other brain imaging or phenotypic data to complement rs-fMRI information appeared promising, achieving higher sensitivities when compared to rs-fMRI data alone (84.7 % versus 72.8 %).  Finally, this analysis showed area under the curve (AUC) values between acceptable and excellent.  The authors concluded that given the many limitations indicated in this study, further well-designed studies are needed to extend the potential use of those classification algorithms to clinical settings, and the quantitative meta-analytical results presented here should be taken with caution.

The authors stated that this study had several drawbacks.  The main one was the sample overlap between the studies, especially considering the lack of information on the patient selection process and the large number of studies that used the ABIDE database.  Sample overlap induced a correlation structure among empirical outcomes, which, if not accounted for, could harm the statistical properties of meta-analysis methods and resulted in higher rates of false positives.  Therefore, it was unclear to which extent this overlap could have biased the results obtained.  In addition, due to the tremendous heterogeneity of ASD, this high degree of overlap may limit the interpretability and generalizability of this analysis.  Despite that, these researchers highlighted that all the significant results obtained in their analyses were reasonable and in line with the literature.  Furthermore, they clearly stated this drawback throughout the study and hoped that it would serve as a guide to future works, eventually reaching a state where more robust analyses could be performed.  These investigators also stated that considering the significant heterogeneity within the selected publications, the summary estimates obtained via the meta-analysis had to be interpreted with caution and in light of the methodologic quality of the studies.  Most studies provided only limited information regarding the patient samples and their clinical characteristics.  However, detailed information regarding the subjects’ disease status, symptoms, current medication, history of interventions, or co-morbidities was crucial for examining the potential of the proposed models to be used in clinical practice.  Therefore, the impact of those variables on classification accuracy needs to be better examined.  The authors stated that the studies included in this analysis identified ASD-distinctive brain patterns as compared to healthy volunteers.  Nevertheless, it is critical to examine the patterns of brain abnormalities that differentiate between different psychiatric disorders.  Additionally, the results obtained in this meta-analysis did not apply to individuals under 5 years of age since almost none of the studies included individuals with such low age.  Furthermore, some methodological steps were not examined in these analyses, such as the data pre-processing and feature selection procedures; these aspects still need to be evaluated to define their effects on classification accuracy.

Emotion-Expressive Suppression

Sikka et al (2022) stated that expressive suppression refers to the inhibition of emotion-expressive behavior (e.g., facial expressions of emotion).  Although it is a commonly used emotion regulation strategy with well-documented consequences for well-being, little is known regarding its underlying mechanisms.  In a systematic review, these investigators synthesized functional neuroimaging studies on the neural bases of expressive suppression in non-clinical populations.  The 12 studies included in this review contrasted the use of expressive suppression to simply watching emotional stimuli.  Results showed that expressive suppression consistently increased activation of frontoparietal regions, especially the dorsolateral and ventrolateral prefrontal cortices and inferior parietal cortex; but decreased activation in temporo-occipital areas.  Results regarding the involvement of the insula and amygdala were inconsistent with studies showing increased, decreased, or no changes in activation.  The authors concluded that these mixed findings underscored the importance of distinguishing expressive suppression from other forms of suppression and highlighted the need to pay more attention to experimental design and neuroimaging data analysis procedures.  They noted that involvement of emotion-generative regions (amygdala and insula) in expressive suppression remains inconclusive; and mixed results stemmed from conceptual and methodological issues that need to be addressed in future research.


References

The above policy is based on the following references:

  1. Alustiza I, Radua J, Pla M, et al. Meta-analysis of functional magnetic resonance imaging studies of timing and cognitive control in schizophrenia and bipolar disorder: Evidence of a primary time deficit. Schizophr Res. 2017;188:21-32.
  2. Anderson DP, Harvey AS, Saling MM, et al. FMRI lateralization of expressive language in children with cerebral lesions. Epilepsia. 2006;47(6):998-1008.
  3. Astrakas LG, Naqvi SH, Kateb B, Tzika AA. Functional MRI using robotic MRI compatible devices for monitoring rehabilitation from chronic stroke in the molecular medicine era (Review). Int J Mol Med. 2012;29(6):963-973.
  4. Atri A, O'Brien JL, Sreenivasan A, et al. Test-retest reliability of memory task functional magnetic resonance imaging in Alzheimer disease clinical trials. Arch Neurol. 2011;68(5):599-606.
  5. Augustovski F, Pichon Riviere, A, Alcaraz A, et al. Functional magnetic resonance imaging for brain pathologies [summary]. Report IRR No. 50. Buenos Aires, Argentina: Institute for Clinical Effectiveness and Health Policy (IECS); 2005.
  6. Benke T, Köylü B, Visani P, et al. Language lateralization in temporal lobe epilepsy: A comparison between fMRI and the Wada Test. Epilepsia. 2006;47(8):1308-1319.
  7. Bookheimer S. Pre-surgical language mapping with functional magnetic resonance imaging. Neuropsychol Rev. 2007;17(2):145-155.
  8. Bukstein O. Adult attention deficit hyperactivity disorder in adults: Epidemiology, pathogenesis, clinical features, course, assessment, and diagnosis. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2014.
  9. Burgmer M, Pogatzki-Zahn E, Gaubitz M, et al. Fibromyalgia unique temporal brain activation during experimental pain: A controlled fMRI Study. J Neural Transm. 2010;117(1):123-131.
  10. Byrne RW. Mapping of eloquent cortex in focal epilepsy with intracranial electrodes. In: Byrne RW, eds. Functional Mapping of the Cerebral Cortex: Safe Surgery in Eloquent Brain. New York, NY; Springer International; 2016.
  11. Campabadal A, Segura B, Junque C, Iranzo A. Structural and functional magnetic resonance imaging in isolated REM sleep behavior disorder: A systematic review of studies using neuroimaging software. Sleep Med Rev. 2021;59:101495.
  12. Canu E, Sarasso E, Filippi M, Agosta F. Effects of pharmacological and nonpharmacological treatments on brain functional magnetic resonance imaging in Alzheimer's disease and mild cognitive impairment: A critical review. Alzheimers Res Ther. 2018;10(1):21.
  13. Caplan LR. Locked-in syndrome. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2015.
  14. Cirstea CM, Brooks WM, Craciunas SC, et al. Primary motor cortex in stroke: A functional MRI-guided proton MR spectroscopic study. Stroke. 2011;42(4):1004-1009.
  15. Corabian P, Hailey D. Functional diagnostic imaging in epilepsy. Health Technology Assessment Series. Edmonton, AB: Alberta Heritage Foundation for Medical Research (AHFMR); 1998.
  16. Davis PC, Seidenwurm DJ, Brunberg JA, et al.; Expert Panel on Neurologic Imaging. Head trauma. ACR Appropriateness Criteria. Reston, VA: American College of Radiology (ACR); 2006.
  17. Di HB, Yu SM, Weng XC, et al. Cerebral response to patient's own name in the vegetative and minimally conscious states. Neurology 2007; 68:895-899.
  18. Filippi M, Rocca MA, Arnold DL, et al. EFNS guidelines on the use of neuroimaging in the management of multiple sclerosis. Eur J Neurol 2006;13(4):313-325.
  19. Gaillard WD, Berl MM, Duke ES, et al. fMRI language dominance and FDG-PET hypometabolism. Neurology. 2011;76(15):1322-1329.
  20. Gaillard WD, Chiron C, Cross JH, et al; ILAE, Committee for Neuroimaging, Subcommittee for Pediatric. Guidelines for imaging infants and children with recent-onset epilepsy. Epilepsia. 2009;50(9):2147-2153.
  21. Gonzalez-Vivas C, Soldevila-Matias P, Sparano O, et al. Longitudinal studies of functional magnetic resonance imaging in first-episode psychosis: A systematic review. Eur Psychiatry. 2019;59:60-69.
  22. Heany SJ, Groenewold NA, Uhlmann A, et al. The neural correlates of childhood trauma questionnaire scores in adults: A meta-analysis and review of functional magnetic resonance imaging studies. Dev Psychopathol. 2018;30(4):1475-1485.
  23. Ibanez V, Deiber MP. Functional imaging in mild cognitive impairment and early Alzheimer's disease: Is it pertinent? Front Neurol Neurosci. 2009;24:30-38.
  24. Karis JP, Seidenwurm DJ, Davis PC, et al.; Expert Panel on Neurologic Imaging. Epilepsy. ACR Appropriateness Criteria. Reston, VA: American College of Radiology (ACR); 2006.
  25. Krull KR. Attention deficit hyperactivity disorder in children and adolescents: Clinical features and evaluation. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2014.
  26. Lu L, Zhang T, Gao R, et al. Mindfulness-based cognitive therapy for obsessive-compulsive disorder: Study protocol for a randomized controlled
    trial with functional magnetic resonance imaging and a 6-month follow-up. J Health Psychol. 2020;25(13-14):2072-2084.
  27. Magland JF, Childress AR. Task-correlated facial and head movements in classifier-based real-time fMRI. J Neuroimaging. 2014;24(4):371-378.
  28. Matchar DB, Kulasingam SL, Huntington A, et al.  Positron emission tomography, single photon emission computed tomography, computed tomography, functional magnetic resonance imaging, and magnetic resonance spectroscopy and for the diagnosis and management of Alzheimer's dementia. Technology Assessment. Prepared by the Duke Center for Clinical Health Policy Research and Evidence Based Center for the Agency for Healthcare Research and Quality (AHRQ). Contract No. 290-02-0025, Task Order # 1. Rockville, MD: AHRQ; April 30, 2004.
  29. Medina LS, Bernal B, Dunoyer C, et al. Seizure disorders: Functional MR imaging for diagnostic evaluation and surgical treatment – prospective study. Radiology. 2005;236(1):247-253.
  30. Ni MF, Zhang BW, Chang Y, et al. Altered resting-state network connectivity in panic disorder: An independent Component Analysis. Brain Imaging
    Behav. 2021;15(3):1313-1322.
  31. Nikolic M, Pezzoli P, Jaworska N, Seto MC. Brain responses in aggression-prone individuals: A systematic review and meta-analysis of functional magnetic resonance imaging (fMRI) studies of anger- and aggression-eliciting tasks. Prog Neuropsychopharmacol Biol Psychiatry. 2022 Jul 5 [Online ahead of print].
  32. O'Connor S, Agius M. A systematic review of structural and functional MRI differences between psychotic and nonpsychotic depression. Psychiatr Danub. 2015;27 Suppl 1:S235-S239.
  33. Ontario Ministry of Health and Long-Term Care, Medical Advisory Secretariat. Functional brain imaging. Health Technology Policy Assessment. Toronto, ON: Ontario Ministry of Health and Long-Term Care; December 2006.
  34. Petrella JR, Shah LM, Harris KM, et al. Preoperative functional MR imaging localization of language and motor areas: Effect on therapeutic decision making in patients with potentially resectable brain tumors. Radiology. 2006;240(3):793-802.
  35. Phillips MD. Functional faults: fMRI in MS. Neurology. 2008;70(4):248-249.
  36. Pouratian N, Bookheimer SY, Rex DE, et al. Utility of preoperative functional magnetic resonance imaging for identifying language cortices in patients with vascular malformations. J Neurosurg. 2002;97(1):21-32.
  37. Prodoehl J, Burciu RG, Vaillancourt DE. Resting state functional magnetic resonance imaging in Parkinson's disease. Curr Neurol Neurosci Rep. 2014;14(6):448.
  38. Rocca MA, Tortorella P, Ceccarelli A, et al. The 'mirror-neuron system' in MS: A 3 tesla fMRI study. Neurology. 2008;70(4):255-262.
  39. Roux FE, Boulanouar K, Lotterie JA, et al. Language functional magnetic resonance imaging in preoperative assessment of language areas: Correlation with direct cortical stimulation. Neurosurgery. 2003;52(6):1335-1345; discussion 1345-1347.
  40. Roy-Byrne PP. Panic disorder in adults: Epidemiology, pathogenesis, clinical manifestations, course, assessment, and diagnosis. UpToDate Inc., Waltham, MA. Last reviewed July 2020.
  41. Sabsevitz DS, Swanson SJ, Hammeke TA, et al. Use of preoperative functional neuroimaging to predict language deficits from epilepsy surgery. Neurology. 2003;60(11):1788-1792.
  42. Santana CP, de Carvalho EA, Rodrigues ID, et al. rs-fMRI and machine learning for ASD diagnosis: A systematic review and meta-analysis. Sci Rep. 2022;12(1):6030.
  43. Schmid E, Thomschewski A, Taylor A, et al; E-PILEPSY consortium. Diagnostic accuracy of functional magnetic resonance imaging, Wada test, magnetoencephalography, and functional transcranial Doppler sonography for memory and language outcome after epilepsy surgery: A systematic review. Epilepsia. 2018;59(12):2305-2317.
  44. Sikka P, Stenberg J, Vorobyev V, Gross JJ. The neural bases of expressive suppression: A systematic review of functional neuroimaging studies. Neurosci Biobehav Rev. 2022;138:104708.
  45. Stancanello J, Cavedon C, Francescon P, et al. BOLD fMRI integration into radiosurgery treatment planning of cerebral vascular malformations. Med Phys. 2007;34(4):1176-1184.
  46. Stender J, Gosseries O, Bruno MA, et al. Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: A clinical validation study. Lancet. 2014;384(9942):514-522.
  47. Stippich C, Rapps N, Dreyhaupt J, et al. Localizing and lateralizing language in patients with brain tumors: Feasibility of routine preoperative functional MR imaging in 81 consecutive patients. Radiology. 2007;243(3):828-836.
  48. Szaflarski JP, Gloss D, Binder JR, et al. Practice guideline summary: Use of fMRI in the presurgical evaluation of patients with epilepsy: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology. 2017;88(4):395-402.
  49. Thompson L, Williams E. Treatment and prognosis of coma in children. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2015.
  50. Tregellas JR, Olincy A, Johnson L, et al. Functional magnetic resonance imaging of effects of a nicotinic agonist in schizophrenia. Neuropsychopharmacology. 2010;35(4):938-942.
  51. University of Michigan Health System. Attention-deficit hyperactivity disorder. Ann Arbor, MI: University of Michigan Health System. April 2013.
  52. Wager TD, Atlas LY, Lindquist MA, et al. An fMRI-based neurologic signature of physical pain. N Engl J Med. 2013;368(15):1388-1397.
  53. Wang HY, Zhang XX, Si CP, et al. Prefrontoparietal dysfunction during emotion regulation in anxiety disorder: A meta-analysis of functional magnetic resonance imaging studies. Neuropsychiatr Dis Treat. 2018;14:1183-1198.
  54. Weinhouse GL, Young GB. Hypoxic-ischemic brain injury: Evaluation and prognosis. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed June 2016.
  55. Whalley HC, Papmeyer M, Sprooten E, et al. Review of functional magnetic resonance imaging studies comparing bipolar disorder and schizophrenia. Bipolar Disord. 2012;14(4):411-431.
  56. Woermann FG, Jokeit H, Luerding R, et al. Language lateralization by Wada test and fMRI in 100 patients with epilepsy. Neurology. 2003;61(5):699-701.
  57. Work Loss Data Institute. Pain (chronic). Encinitas, CA: Work Loss Data Institute. November 14, 2013.
  58. Young CB. Stupor and coma in adults. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2015.