A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Consultation-Liaison Case Conference: Overcoming Bias in the Differential Diagnosis of Psychosis

We present the case of a 34-year-old Black patient with no significant psychiatric history who presented with catatonia and psychotic symptoms following a recent severe acute respiratory syndrome coronavirus-2 infection, whose diagnosis of coronavirus disease 2019 encephalitis was delayed by premature attribution of his symptoms to a primary psychiatric etiology. Top experts in the consultation-liaison field provide guidance for this commonly encountered clinical case based on their experience and a review of the available literature. Key teaching topics include the diagnosis and management of coronavirus disease 2019 encephalitis, cognitive bias, and racial bias. Specifically, this case illustrates the role of the consultation-liaison psychiatrist in identifying medical conditions that may overlap with psychiatric presentations and in advocating for marginalized patients.

Mapping the Relationship of White Matter Lesions to Depression in Multiple Sclerosis

BACKGROUND

Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption is related to depression in MS.

METHODS

Using electronic health records, 380 participants with MS were identified. Depressed individuals (MS+Depression group; n = 232) included persons who had an ICD-10 depression diagnosis, had a prescription for antidepressant medication, or screened positive via Patient Health Questionnaire (PHQ)-2 or PHQ-9. Age- and sex-matched nondepressed individuals with MS (MS-Depression group; n = 148) included persons who had no prior depression diagnosis, had no psychiatric medication prescriptions, and were asymptomatic on PHQ-2 or PHQ-9. Research-quality 3T structural magnetic resonance imaging was obtained as part of routine care. We first evaluated whether lesions were preferentially located within the depression network compared with other brain regions. Next, we examined if MS+Depression patients had greater lesion burden and if this was driven by lesions in the depression network. Primary outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain.

RESULTS

MS lesions preferentially affected fascicles within versus outside the depression network (β = 0.09, 95% CI = 0.08 to 0.10, p < .001). MS+Depression patients had more lesion burden (β = 0.06, 95% CI = 0.01 to 0.10, p = .015); this was driven by lesions within the depression network (β = 0.02, 95% CI = 0.003 to 0.040, p = .020).

CONCLUSIONS

We demonstrated that lesion location and burden may contribute to depression comorbidity in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression patients had more disease than MS-Depression patients, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.

Network Enrichment Significance Testing in Brain-Phenotype Association Studies

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about the spatial structure of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genomics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose Network Enrichment Significance Testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study phenotype associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

Individual differences in delay discounting are associated with dorsal prefrontal cortex connectivity in children, adolescents, and adults

Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including obesity and academic achievement. However, resting-state functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of children, adolescents, and adults. A total of 293 participants (9-23 years) completed a delay discounting task and underwent 3T resting-state fMRI. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a default mode network hub. Greater delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other default mode network regions, but reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest delay discounting in children, adolescents, and adults is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.

Development of Iron Status Measures during Youth: Associations with Sex, Neighborhood Socioeconomic Status, Cognitive Performance, and Brain Structure

BACKGROUND

Iron is essential to brain function, and iron deficiency during youth may adversely impact neurodevelopment. Understanding the developmental time course of iron status and its association with neurocognitive functioning is important for identifying windows for intervention.

OBJECTIVES

This study aimed to characterize developmental change in iron status and understand its association with cognitive performance and brain structure during adolescence using data from a large pediatric health network.

METHODS

This study included a cross-sectional sample of 4899 participants (2178 males; aged 8-22 y at the time of participation, M [SD] = 14.24 [3.7]) who were recruited from the Children's Hospital of Philadelphia network. Prospectively collected research data were enriched with electronic medical record data that included hematological measures related to iron status, including serum hemoglobin, ferritin, and transferrin (33,015 total samples). At the time of participation, cognitive performance was assessed using the Penn Computerized Neurocognitive Battery, and brain white matter integrity was assessed using diffusion-weighted MRI in a subset of individuals.

RESULTS

Developmental trajectories were characterized for all metrics and revealed that sex differences emerged after menarche such that females had reduced iron status relative to males [all R2partial > 0.008; all false discovery rates (FDRs) < 0.05]. Higher socioeconomic status was associated with higher hemoglobin concentrations throughout development (R2partial = 0.005; FDR < 0.001), and the association was greatest during adolescence. Higher hemoglobin concentrations were associated with better cognitive performance during adolescence (R2partial = 0.02; FDR < 0.001) and mediated the association between sex and cognition (mediation effect = -0.107; 95% CI: -0.191, -0.02). Higher hemoglobin concentration was also associated with greater brain white matter integrity in the neuroimaging subsample (R2partial = 0.06, FDR = 0.028).

CONCLUSIONS

Iron status evolves during youth and is lowest in females and individuals of low socioeconomic status during adolescence. Diminished iron status during adolescence has consequences for neurocognition, suggesting that this critical period of neurodevelopment may be an important window for intervention that has the potential to reduce health disparities in at-risk populations.

Mapping the relationship of white matter lesions to depression in multiple sclerosis

Importance

Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression.

Objective

To investigate how white matter network disruption is related to depression in MS.

Design

Retrospective case-control study of participants who received research-quality 3-tesla neuroimaging as part of MS clinical care from 2010-2018. Analyses were performed from May 1 to September 30, 2022.

Setting

Single-center academic medical specialty MS clinic.

Participants

Participants with MS were identified via the electronic health record (EHR). All participants were diagnosed by an MS specialist and completed research-quality MRI at 3T. After excluding participants with poor image quality, 783 were included. Inclusion in the depression group (MS+Depression) required either: 1) ICD-10 depression diagnosis (F32-F34.*); 2) prescription of antidepressant medication; or 3) screening positive via Patient Health Questionnaire-2 (PHQ-2) or -9 (PHQ-9). Age- and sex-matched nondepressed comparators (MS-Depression) included persons with no depression diagnosis, no psychiatric medications, and were asymptomatic on PHQ-2/9.

Exposure

Depression diagnosis.

Main Outcomes and Measures

We first evaluated if lesions were preferentially located within the depression network compared to other brain regions. Next, we examined if MS+Depression patients had greater lesion burden, and if this was driven by lesions specifically in the depression network. Outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. Secondary measures included between-diagnosis lesion burden, stratified by brain network. Linear mixed-effects models were employed.

Results

Three hundred-eighty participants met inclusion criteria, (232 MS+Depression: age[SD]=49[12], %females=86; 148 MS-Depression: age[SD]=47[13], %females=79). MS lesions preferentially affected fascicles within versus outside the depression network (β=0.09, 95% CI=0.08-0.10, P<0.001). MS+Depression had more white matter lesion burden (β=0.06, 95% CI=0.01-0.10, P=0.015); this was driven by lesions within the depression network (β=0.02, 95% CI 0.003-0.040, P=0.020).

Conclusions and Relevance

We provide new evidence supporting a relationship between white matter lesions and depression in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression had more disease than MS-Depression, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.

Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence

With the increasing availability of neuroimaging data from multiple modalities-each providing a different lens through which to study brain structure or function-new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.

Voxel-wise intermodal coupling analysis of two or more modalities using local covariance decomposition

When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities - that is, in how modalities covary at the voxel level. Previous studies have shown that local covariance structures between modalities, or intermodal coupling (IMCo), can be summarized for two modalities, and that two-modality IMCo reveals otherwise undiscovered patterns in neurodevelopment and certain diseases. However, previous IMCo methods are based on the slopes of local weighted linear regression lines, which are inherently asymmetric and limited to the two-modality setting. Here, we present a generalization of IMCo estimation which uses local covariance decompositions to define a symmetric, voxel-wise coupling coefficient that is valid for two or more modalities. We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi-modal data continues to increase, principal-component-based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github.com/hufengling/pIMCo.

ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

Subgenual cingulate resting regional cerebral blood flow in premenstrual dysphoric disorder: differential regulation by ovarian steroids and preliminary evidence for an association with expression of ESC/E(Z) complex genes

Substantial evidence suggests that circulating ovarian steroids modulate behavior differently in women with PMDD than in those without this condition. However, hormonal state-related abnormalities of neural functioning in PMDD remain to be better characterized. In addition, while altered neural function in PMDD likely co-exists with alterations in intrinsic cellular function, such a relationship has not been explored. Here, we investigated the effects of ovarian steroids on basal, resting regional cerebral blood flow (rCBF) in PMDD, and, in an exploratory analysis, we tested whether the rCBF findings were linked to the expression of ESC/E(Z) genes, which form an essential ovarian steroid-regulated gene-silencing complex. Resting rCBF was measured with oxygen-15 water PET (189 PET sessions in 43 healthy women and 20 women with PMDD) during three self-as-own-control conditions: GnRH agonist (Lupron)-induced ovarian suppression, estradiol add-back, and progesterone add-back. ESC/E(Z) gene expression data were obtained from RNA-sequencing of lymphoblastoid cell lines performed in a previous study and were examined in relation to hormone-induced changes in rCBF. In the rCBF PET data, there was a significant diagnosis-by-hormone interaction in the subgenual cingulate (P_FDR = 0.05), an important neuroanatomical hub for regulating affective state. Whereas control women showed no hormonally-related changes in resting rCBF, those with PMDD showed decreased resting rCBF during both estradiol (P = 0.02) and progesterone (P = 0.0002) add-back conditions. In addition, in PMDD, ESC/E(Z) gene expression correlated with the change in resting rCBF between Lupron-alone and progesterone conditions (Pearson r = -0.807, P = 0.016). This work offers a formulation of PMDD that integrates behavioral, neural circuit, and cellular mechanisms, and may provide new targets for future therapeutic interventions.

Neurocognitive and functional heterogeneity in depressed youth

Depression is a common psychiatric illness that often begins in youth, and is sometimes associated with cognitive deficits. However, there is significant variability in cognitive dysfunction, likely reflecting biological heterogeneity. We sought to identify neurocognitive subtypes and their neurofunctional signatures in a large cross-sectional sample of depressed youth. Participants were drawn from the Philadelphia Neurodevelopmental Cohort, including 712 youth with a lifetime history of a major depressive episode and 712 typically developing (TD) youth matched on age and sex. A subset (MDD n = 368, TD n = 200) also completed neuroimaging. Cognition was assessed with the Penn Computerized Neurocognitive Battery. A recently developed semi-supervised machine learning algorithm was used to delineate neurocognitive subtypes. Subtypes were evaluated for differences in both clinical psychopathology and brain activation during an n-back working memory fMRI task. We identified three neurocognitive subtypes in the depressed group. Subtype 1 was high-performing (high accuracy, moderate speed), Subtype 2 was cognitively impaired (low accuracy, slow speed), and Subtype 3 was impulsive (low accuracy, fast speed). While subtypes did not differ in clinical psychopathology, they diverged in their activation profiles in regions critical for executive function, which mirrored differences in cognition. Taken together, these data suggest disparate mechanisms of cognitive vulnerability and resilience in depressed youth, which may inform the identification of biomarkers for prognosis and treatment response.

Brain Responses to Noxious Stimuli in Patients With Chronic Pain: A Systematic Review and Meta-analysis

IMPORTANCE

Functional neuroimaging is a valuable tool for understanding how patients with chronic pain respond to painful stimuli. However, past studies have reported heterogenous results, highlighting opportunities for a quantitative meta-analysis to integrate existing data and delineate consistent associations across studies.

OBJECTIVE

To identify differential brain responses to noxious stimuli in patients with chronic pain using functional magnetic resonance imaging (fMRI) while adhering to current best practices for neuroimaging meta-analyses.

DATA SOURCES

All fMRI experiments published from January 1, 1990, to May 28, 2019, were identified in a literature search of PubMed/MEDLINE, EMBASE, Web of Science, Cochrane Library, PsycINFO, and SCOPUS.

STUDY SELECTION

Experiments comparing brain responses to noxious stimuli in fMRI between patients and controls were selected if they reported whole-brain results, included at least 10 patients and 10 healthy control participants, and used adequate statistical thresholding (voxel-height P < .001 or cluster-corrected P < .05). Two independent reviewers evaluated titles and abstracts returned by the search. In total, 3682 abstracts were screened, and 1129 full-text articles were evaluated.

DATA EXTRACTION AND SYNTHESIS

Thirty-seven experiments from 29 articles met inclusion criteria for meta-analysis. Coordinates reporting significant activation differences between patients with chronic pain and healthy controls were extracted. These data were meta-analyzed using activation likelihood estimation. Data were analyzed from December 2019 to February 2020.

MAIN OUTCOMES AND MEASURES

A whole-brain meta-analysis evaluated whether reported differences in brain activation in response to noxious stimuli between patients and healthy controls were spatially convergent. Follow-up analyses examined the directionality of any differences. Finally, an exploratory (nonpreregistered) region-of-interest analysis examined differences within the pain network.

RESULTS

The 37 experiments from 29 unique articles included a total of 511 patients and 433 controls (944 participants). Whole-brain meta-analyses did not reveal significant differences between patients and controls in brain responses to noxious stimuli at the preregistered statistical threshold. However, exploratory analyses restricted to the pain network revealed aberrant activity in patients.

CONCLUSIONS AND RELEVANCE

In this systematic review and meta-analysis, preregistered, whole-brain analyses did not reveal aberrant fMRI activity in patients with chronic pain. Exploratory analyses suggested that subtle, spatially diffuse differences may exist within the pain network. Future work on chronic pain biomarkers may benefit from focus on this core set of pain-responsive areas.

Neurocovid: Pharmacological Recommendations for Delirium Associated With COVID-19

Background

The pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as one of the biggest health threats of our generation. A significant portion of patients are presenting with delirium and neuropsychiatric sequelae of the disease. Unique examination findings and responses to treatment have been identified.

Objective

In this article, we seek to provide pharmacologic and treatment recommendations specific to delirium in patients with COVID-19.

Methods

We performed a literature search reviewing the neuropsychiatric complications and treatments in prior coronavirus epidemics including Middle Eastern respiratory syndrome and severe acute respiratory syndrome coronaviruses, as well as the emerging literature regarding COVID-19. We also convened a work group of consultation-liaison psychiatrists actively managing patients with COVID-19 in our hospital. Finally, we synthesized these findings to provide preliminary pharmacologic recommendations for treating delirium in these patients.

Results

Delirium is frequently found in patients who test positive for COVID-19, even in the absence of respiratory symptoms. There appears to be a higher rate of agitation, myoclonus, abulia, and alogia. No data are currently available on the treatment of delirium in patients with COVID-19. Extrapolating from general delirium treatment, Middle Eastern respiratory syndrome/severe acute respiratory syndrome case reports, and our experience, preliminary recommendations for pharmacologic management have been assembled.

Conclusions

COVID-19 is associated with neuropsychiatric symptoms. Low-potency neuroleptics and alpha-2 adrenergic agents may be especially useful in this setting. Further research into the pathophysiology of COVID-19 will be key in developing more targeted treatment guidelines.

Neurostructural Heterogeneity in Youths With Internalizing Symptoms

BACKGROUND

Internalizing disorders such as anxiety and depression are common psychiatric disorders that frequently begin in youth and exhibit marked heterogeneity in treatment response and clinical course. Given that symptom-based classification approaches do not align with underlying neurobiology, an alternative approach is to identify neurobiologically informed subtypes based on brain imaging data.

METHODS

We used a recently developed semisupervised machine learning method (HYDRA [heterogeneity through discriminative analysis]) to delineate patterns of neurobiological heterogeneity within youths with internalizing symptoms using structural data collected at 3T from a sample of 1141 youths.

RESULTS

Using volume and cortical thickness, cross-validation methods indicated 2 highly stable subtypes of internalizing youths (adjusted Rand index = 0.66; permutation-based false discovery rate p < .001). Subtype 1, defined by smaller brain volumes and reduced cortical thickness, was marked by impaired cognitive performance and higher levels of psychopathology than both subtype 2 and typically developing youths. Using resting-state functional magnetic resonance imaging and diffusion images not considered during clustering, we found that subtype 1 also showed reduced amplitudes of low-frequency fluctuations in frontolimbic regions at rest and reduced fractional anisotropy in several white matter tracts. In contrast, subtype 2 showed intact cognitive performance and greater volume, cortical thickness, and amplitudes during rest compared with subtype 1 and typically developing youths, despite still showing clinically significant levels of psychopathology.

CONCLUSIONS

We identified 2 subtypes of internalizing youths differentiated by abnormalities in brain structure, function, and white matter integrity, with one of the subtypes showing poorer functioning across multiple domains. Identification of biologically grounded internalizing subtypes may assist in targeting early interventions and assessing longitudinal prognosis.

Convergent neural representations of experimentally-induced acute pain in healthy volunteers: A large-scale fMRI meta-analysis

Characterizing a reliable, pain-related neural signature is critical for translational applications. Many prior fMRI studies have examined acute nociceptive pain-related brain activation in healthy participants. However, synthesizing these data to identify convergent patterns of activation can be challenging due to the heterogeneity of experimental designs and samples. To address this challenge, we conducted a comprehensive meta-analysis of fMRI studies of stimulus-induced pain in healthy participants. Following pre-registration, two independent reviewers evaluated 4,927 abstracts returned from a search of 8 databases, with 222 fMRI experiments meeting inclusion criteria. We analyzed these experiments using Activation Likelihood Estimation with rigorous type I error control (voxel height p < 0.001, cluster p < 0.05 FWE-corrected) and found a convergent, largely bilateral pattern of pain-related activation in the secondary somatosensory cortex, insula, midcingulate cortex, and thalamus. Notably, these regions were consistently recruited regardless of stimulation technique, location of induction, and participant sex. These findings suggest a highly-conserved core set of pain-related brain areas, encouraging applications as a biomarker for novel therapeutics targeting acute nociceptive pain.

Abnormalities of dorsolateral prefrontal function in women with premenstrual dysphoric disorder: a multimodal neuroimaging study

OBJECTIVE

To investigate the neural substrate of premenstrual dysphoric disorder (PMDD), the authors used [15O]H2O positron emission tomography (PET) regional cerebral blood flow (rCBF) and blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal measurements during working memory in conjunction with a 6-month hormone manipulation protocol.

METHOD

PET and fMRI scans were obtained from women with prospectively confirmed PMDD and asymptomatic comparison subjects while they completed the n-back task during three hormone conditions: ovarian suppression induced by the gonadotropin-releasing hormone agonist leuprolide acetate, leuprolide plus estradiol, and leuprolide plus progesterone. Fifteen patients and 15 matched comparison subjects underwent PET imaging. Fourteen patients and 14 comparison subjects underwent fMRI. For each hormone condition, rCBF was measured with [15O]H2O PET, and BOLD signal was measured with fMRI, both during an n-back working memory paradigm. Global Assessment of Functioning Scale (GAF) scores and clinical characteristics were obtained for each patient before hormone manipulation, and symptoms were measured before and during the protocol.

RESULTS

In both the PET and fMRI studies, a main effect of diagnosis was observed, with PMDD patients showing greater prefrontal activation than comparison subjects. In the patient group, the degree to which dorsolateral prefrontal cortex activation was abnormally increased correlated with several dimensions of disease: disability as indicated by GAF scores, age at symptom onset, duration of PMDD, and differences in pre- and postmenses PMDD symptoms.

CONCLUSIONS

Abnormal working memory activation in PMDD, specifically in the dorsolateral prefrontal cortex, is related to PMDD severity, symptoms, age at onset, and disease burden. These results support the clinical relevance of the findings and the proposal that dorsolateral prefrontal cortex dysfunction represents a substrate of risk for PMDD. The concordance of the fMRI and PET data attests to the neurobiological validity of the results.