Limbic Networks Research Articles

Automatic classification of AD pathology in FTD phenotypes using natural speech.

Screening for Alzheimer's disease neuropathologic change (ADNC) in individuals with atypical presentations is challenging but essential for clinical management. We trained automatic speech-based classifiers to distinguish frontotemporal dementia (FTD) patients with ADNC from those with frontotemporal lobar degeneration (FTLD). We trained automatic classifiers with 99 speech features from 1 minute speech samples of 179 participants (ADNC=36, FTLD=60, healthy controls [HC]=89). Patients' pathology was assigned based on autopsy or cerebrospinal fluid analytes. Structural network-based magnetic resonance imaging analyses identified anatomical correlates of distinct speech features. Our classifier showed 0.88 0.03 area under the curve (AUC) for ADNC versusFTLD and 0.93 0.04 AUC for patients versusHC. Noun frequency and pause rate correlated with gray matter volume loss in the limbic and salience networks, respectively. Brief naturalistic speech samples can be used for screening FTD patients for underlying ADNC in vivo. This work supports the future development of digital assessment tools for FTD. We trained machine learning classifiers for frontotemporal dementia patients using natural speech. We grouped participants by neuropathological diagnosis (autopsy) or cerebrospinal fluid biomarkers. Classifiers well distinguished underlying pathology (Alzheimer's disease vs. frontotemporal lobar degeneration) in patients. We identified important features through an explainable artificial intelligence approach. This work lays the groundwork for a speech-based neuropathology screening tool.

Mapping structural covariance networks of emotional withdrawal symptoms in males with methamphetamine use disorder during abstinence.

Individuals with methamphetamine use disorder (MUD) often experience anxiety and depressive symptoms during abstinence, which can worsen the likelihood of relapse. Thus, it is essential to understand the neuro-mechanism behind methamphetamine use and its associated emotional withdrawal symptoms in order to develop effective clinical strategies. This study aimed to evaluate associations between emotional withdrawal symptoms and structural covariance networks (SCNs) based on cortical thickness (CTh) across the brain. The CTh measures were obtained from Tl-weighted MRI data from a sample of 48 males with MUD during abstinence and 48 male healthy controls. The severity of anxiety and depressive symptoms was assessed by the Hamilton Anxiety Scale (HAMA) and depression (HAMD) scales. Two important nodes belonging to the brain reward system, the right rostral anterior cingulate cortex (rACC) and medial prefrontal cortex (medPFC), were selected as seeds to conduct SCNs and modulation analysis by emotional symptoms. MUDs showed higher structural covariance between the right rACC and regions in the dorsal attention, right frontoparietal, auditory, visual and limbic networks. They also displayed higher structural covariance between the right medPFC and regions in the limbic network. Moreover, the modulation analysis showed that higher scores on HAMA were associated with increased covariance between the right rACC and the left parahippocampal and isthmus cingulate cortex in the default mode network. These outcomes shed light on the complex neurobiological mechanisms underlying methamphetamine use and its associated emotional withdrawal symptoms and may provide new insights into the development of effective treatments for MUD.

Structural white matter properties and cognitive resilience to tau pathology.

We assessed whether macro- and/or micro-structural white matter properties are associated with cognitive resilience to Alzheimer's disease pathology years prior to clinical onset. We examined whether global efficiency, an indicator of communication efficiency in brain networks, and diffusion measurements within the limbic network and default mode network moderate the association between amyloid-β/tau pathology and cognitive decline. We also investigated whether demographic and health/risk factors are associated with white matter properties. Higher global efficiency of the limbic network, as well as free-water corrected diffusion measures within the tracts of both networks, attenuated the impact of tau pathology on memory decline. Education, age, sex, white matter hyperintensities, and vascular risk factors were associated with white matter properties of both networks. White matter can influence cognitive resilience against tau pathology, and promoting education and vascular health may enhance optimal white matter properties. Aβ and tau were associated with longitudinal memory change over ∼7.5 years. White matter properties attenuated the impact of tau pathology on memory change. Health/risk factors were associated with white matter properties.

Whole-brain dynamics across the menstrual cycle: the role of hormonal fluctuations and age in healthy women

Recent neuroimaging research suggests that female sex hormone fluctuations modulate brain activity. Nevertheless, how brain network dynamics change across the female menstrual cycle remains largely unknown. Here, we investigated the dynamical complexity underlying three menstrual cycle phases (i.e., early follicular, pre-ovulatory, and mid-luteal) in 60 healthy naturally-cycling women scanned using resting-state fMRI. Our results revealed that the pre-ovulatory phase exhibited the highest dynamical complexity (variability over time) across the whole-brain functional network compared to the early follicular and mid-luteal phases, while the early follicular showed the lowest. Furthermore, we found that large-scale resting-state networks reconfigure along menstrual cycle phases. Multilevel mixed-effects models revealed age-related changes in the whole-brain, control, and dorsal attention networks, while estradiol and progesterone influenced the whole-brain, DMN, limbic, dorsal attention, somatomotor, and subcortical networks. Overall, these findings evidence that age and ovarian hormones modulate brain network dynamics along the menstrual cycle.

Brain magnetic resonance imaging outperforms clinical severity ratings in the prediction of treatment outcomes in major depressive disorder

IntroductionMajor depressive disorder (MDD) is a prevalent and disabling condition. Approximately 30-50% of patients do not respond to first-line medication or psychotherapy. Therefore, several studies have investigated the predictive potential of pretreatment severity rating or neuroimaging features to guide clinical approaches that can speed optimal treatment selection.ObjectivesTo evaluate the performance of 1) severity ratings (scores of Hamilton Depression/Anxiety Scale, illness duration, and sleep quality, etc.) and demographic characteristic and 2) brain magnetic resonance imaging (MRI) features in predicting treatment outcomes for MDD. Second, to assess performance variations among varied modalities and interventions in MRI studies.MethodsWe searched studies in PubMed, Embase, Web of Science, and Science Direct databases before March 22, 2023. We extracted a confusion matrix for prediction in each study. Separate meta-analyses were performed for clinical and MRI studies. The logarithm of diagnostic odds ratio [log(DOR)], sensitivity, and specificity were conducted using Reitsma’s random effect model. The area under curve (AUC) of summary receiver operating characteristic (SROC) curve was calculated.Subgroup analyses were conducted in MRI studies based on modalities: resting-state functional MRI (rsfMRI), task-based fMRI (tbfMRI), and structural MRI (sMRI), and interventions: antidepressant (including selective serotonin reuptake inhibitors [SSRI]) and electroconvulsive therapy (ECT). Meta-regression was conducted 1) between clinical and MRI studies and 2) among modality or intervention subgroups in MRI studies.ResultsWe included ten studies used clinical features covering 6494 patients, yielded a log(DOR) of 1.42, AUC of 0.71, sensitivity of 0.61, and specificity of 0.74. In terms of MRI, 44 studies with 2623 patients were included, revealing an overall log(DOR) of 2.53. The AUC, sensitivity, and specificity were 0.89, 0.78, and 0.75.Studies using MRI features had a higher sensitivity (0.89 vs. 0.61) in predicting treatment outcomes than clinical features (P < 0.001). RsfMRI had higher specificity (0.79 vs. 0.69) than tbfMRI subgroup (P = 0.01). No significant differences were found between sMRI and other modalities, nor between antidepressants (SSRIs and others) and ECT. Antidepressant studies primarily identified predictive imaging features in limbic and default mode networks, while ECT mainly focused on limbic network.ConclusionsOur findings suggest a robust promise for pretreatment brain MRI features in predicting treatment outcomes in MDD, offering higher accuracy than clinical studies. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. For MRI studies, overlapping but distinct network level measures predicted outcomes for antidepressants and ECT.Disclosure of InterestNone Declared

Functional brain network controllability dysfunction in Alzheimer’s disease and its relationship with cognition and gene expression profiling

Objective. In recent studies, network control theory has been applied to clarify transitions between brain states, emphasizing the significance of assessing the controllability of brain networks in facilitating transitions from one state to another. Despite these advancements, the potential alterations in functional network controllability associated with Alzheimer’s disease (AD), along with the underlying genetic mechanisms responsible for these alterations, remain unclear. Approach. We conducted a comparative analysis of functional network controllability measures between patients with AD (n = 64) and matched normal controls (NCs, n = 64). We investigated the association between altered controllability measures and cognitive function in AD. Additionally, we conducted correlation analyses in conjunction with the Allen Human Brain Atlas to identify genes whose expression was correlated with changes in functional network controllability in AD, followed by a set of analyses on the functional features of the identified genes. Main results. In comparison to NCs, patients with AD exhibited a reduction in average controllability, predominantly within the default mode network (DMN) (63% of parcellations), and an increase in average controllability within the limbic (LIM) network (33% of parcellations). Conversely, AD patients displayed a decrease in modal controllability within the LIM network (27% of parcellations) and an increase in modal controllability within the DMN (80% of parcellations). In AD patients, a significant positive correlation was found between the average controllability of the salience network and the mini-mental state examination scores. The changes in controllability measures exhibited spatial correlation with transcriptome profiles. The significant genes identified exhibited enrichment in neurobiologically relevant pathways and demonstrated preferential expression in various tissues, cell types, and developmental periods. Significance. Our findings have the potential to offer new insights into the genetic mechanisms underlying alterations in the controllability of functional networks in AD. Additionally, these results offered perspectives for a deeper understanding of the pathogenesis and the development of therapeutic strategies for AD.

White matter structural changes before and after microvascular decompression for hemifacial spasm.

Hemifacial spasm (HFS) is a syndrome characterized by involuntary contractions of the facial muscles innervated by the ipsilateral facial nerve. Currently, microvascular decompression (MVD) is an effective treatment for HFS. Diffusion weighted imaging (DWI) is a non-invasive advanced magnetic resonance technique that allows us to reconstruct white matter (WM) virtually based on water diffusion direction. This enables us to model the human brain as a complex network using graph theory. In our study, we recruited 32 patients with HFS and 32 healthy controls to analyze and compare the topological organization of whole-brain white matter networks between the groups. We also explored the potential relationships between altered topological properties and clinical outcomes. Compared to the HC group, the white matter network was disrupted in both preoperative and postoperative groups of HFS patients, mainly located in the somatomotor network, limbic network, and default network (All P < 0.05, FDR corrected). There was no significant difference between the preoperative and postoperative groups (P > 0.05, FDR corrected). There was a correlation between the altered topological properties and clinical outcomes in the postoperative group of patients (All P < 0.05, FDR corrected). Our findings indicate that in HFS, the white matter structural network was disrupted before and after MVD, and that these alterations in the postoperative group were correlated with the clinical outcomes. White matter alteration here described may subserve as potential biomarkers for HFS and may help us identify patients with HFS who can benefit from MVD and thus can help us make a proper surgical patient selection.

Topological features of functional brain networks and subclinical impulsivity: an investigation in younger and older adults.

Impulsive traits (i.e., the tendency to act without forethought regardless of negative outcomes) are frequently found in healthy populations. When exposed to risk factors, individuals may develop debilitating disorders of impulse control (addiction, substance abuse, gambling) characterized by behavioral and cognitive deficits, eventually leading to huge socioeconomic costs. With the far-reaching aim of preventing the onset of impulsive disorders, it is relevant to investigate the topological organization of functional brain networks associated with impulsivity in sub-clinical populations. Taking advantage of the open-source LEMON dataset, we investigated the topological features of resting-state functional brain networks associated with impulsivity in younger (n=146, age: 20-35) and older (n=61, age: 59-77) individuals, using a graph-theoretical approach. Specifically, we computed indices of segregation and integration at the level of specific circuits and nodes known to be involved in impulsivity (frontal, limbic, and striatal networks). In younger individuals, results revealed that impulsivity was associated with a more widespread, less clustered and less efficient functional organization, at all levels of analyses and in all selected networks. Conversely, impulsivity in older individuals was associated with reduced integration and increased segregation of striatal regions. Speculatively, such alterations of functional brain networks might underlie behavioral and cognitive abnormalities associated with impulsivity, a working hypothesis worth being tested in future research. Lastly, differences between younger and older individuals might reflect the implementation of age-specific adaptive strategies, possibly accounting for observed differences in behavioral manifestations. Potential interpretations, limitations and implications are discussed.

The cognitive relevance of non-lesional damage to cortical networks in people with multiple sclerosis.

Cognitive impairment, a common and debilitating symptom in people with multiple sclerosis (MS), is especially related to cortical damage. However, the impact of regional cortical damage remains poorly understood. Our aim was to evaluate structural (network) integrity in lesional and non-lesional cortex in people with MS, and its relationship with cognitive dysfunction. In this cross-sectional study, 176 people with MS and 48 healthy controls underwent MRI, including double inversion recovery and diffusion-weighted scans, and neuropsychological assessment. Cortical integrity was assessed based on fractional anisotropy (FA) and mean diffusivity (MD) within 212 regions split into lesional or non-lesional cortex, and grouped into seven cortical networks. Integrity was compared between people with MS and controls, and across cognitive groups: cognitively-impaired (CI; ≥ two domains at Z ≤ -2 below controls), mildly CI (≥ two at -2 < Z ≤ -1.5), or cognitively-preserved (CP). Cortical lesions were observed in 87.5% of people with MS, mainly in ventral attention network, followed by limbic and default mode networks. Compared to controls, in non-lesional cortex, MD was increased in people with MS, but mean FA did not differ. Within the same individual, MD and FA were increased in lesional compared to non-lesional cortex. CI-MS exhibited higher MD than CP-MS in non-lesional cortex of default mode, frontoparietal and sensorimotor networks, of which the default mode network could best explain cognitive performance. Diffusion differences in lesional cortex were more severe than in non-lesional cortex. However, while most people with MS had cortical lesions, diffusion differences in CI-MS were more prominent in non-lesional cortex than lesional cortex, especially within default mode, frontoparietal and sensorimotor networks.

Alcohol attention bias modulates neural engagement during moral processing

ABSTRACT The neurobiology of typical moral cognition involves the interaction of frontal, limbic, and temporoparietal networks. There is still much to be understood mechanistically about how moral processing is disrupted; such understanding is key to combating antisociality. Neuroscientific models suggest a key role for attention mechanisms in atypical moral processing. We hypothesized that attention-bias toward alcohol cues in alcohol use disorder (AUD) leads to a failure to properly engage with morally relevant stimuli, reducing moral processing. We recruited patients with AUD (n = 30) and controls (n = 30). During functional magnetic resonance imaging, participants viewed pairs of images consisting of a moral or neutral cue and an alcohol or neutral distractor. When viewing moral cues paired with alcohol distractors, individuals with AUD had lower medial prefrontal cortex engagement; this pattern was also seen for left amygdala in younger iAUDs. Across groups, individuals had less engagement of middle/superior temporal gyri. These findings provide initial support for AUD-related attention bias interference in sociomoral processing. If supported in future longitudinal and causal study designs, this finding carries potential societal and clinical benefits by suggesting a novel, leverageable mechanism and in providing a cognitive explanation that may help combat persistent stigma.

Fatigue and perceived energy in a sample of older adults over 10 years: A resting state functional connectivity study of neural correlates

PurposeDeclining energy and increasing fatigue, common in older age, predict neurodegenerative conditions, but their neural substrates are not known. We examined brain resting state connectivity in relation to declining self-reported energy levels (SEL) and occurrence of fatigue over time. MethodsWe examined resting-state functional MRI in 272 community dwelling older adults participating in the Health Aging and Body Composition Study (mean age 83 years; 57.4 % female; 40.8 % Black) with measures of fatigue and SEL collected at regular intervals over the prior ten years. Functional connectivity (FC) between cortex and striatum was examined separately for sensorimotor, executive, and limbic functional subregions. Logistic regression tested the association of FC in each network with prior fatigue state (reporting fatigue at least once or never reporting fatigue), and with SEL decline (divided into stable or declining SEL groups) and adjusted for demographic, physical function, mood, cognition, and comorbidities. ResultsHigher cortico-striatal FC in the right limbic network was associated with lower odds of reporting fatigue (better) at least once during the study period (adjusted odds ratio [95 % confidence interval], p-value: (0.747 [0.582, 0.955], 0.020), independent of SEL. Higher cortico-striatal FC in the right executive network was associated with higher odds of declining SEL (worse) during the study period (adjusted odds ratio [95 % confidence interval], p-value: (1.31 [1.01, 1.69], 0.041), independent of fatigue. Associations with other networks were not significant. ConclusionsIn this cohort of older adults, the cortico-striatal functional connectivity of declining SEL appears distinct from that underlying fatigue. Studies to further assess the neural correlates of energy and fatigue, and their independent contribution to neurodegenerative conditions are warranted.

Cortical thickness reveals sex differences in verbal and visuospatial memory.

Although previous studies have reported the sex differences in behavior/cognition and the brain, the sex difference in the relationship between memory abilities and the underlying neural basis in the aging process remains unclear. In this study, we used a machine learning model to estimate the association between cortical thickness and verbal/visuospatial memory in females and males and then explored the sex difference of these associations based on a community-elderly cohort (n = 1153, age ranged from 50.42 to 86.67years). We validated that females outperformed males in verbal memory, while males outperformed females in visuospatial memory. The key regions related to verbal memory in females include the medial temporal cortex, orbitofrontal cortex, and some regions around the insula. Further, those regions are more located in limbic, dorsal attention, and default-model networks, and are associated with face recognition and perception. The key regions related to visuospatial memory include the lateral prefrontal cortex, anterior cingulate gyrus, and some occipital regions. They overlapped more with dorsal attention, frontoparietal and visual networks, and were associated with object recognition. These findings imply the memory performance advantage of females and males might be related to the different memory processing tendencies and their associated network.

Structure-Function Interactions in the Hippocampus and Prefrontal Cortex Are Associated with Episodic Memory in Healthy Aging.

Aging comes with declines in episodic memory. Memory decline is accompanied by structural and functional alterations within key brain regions, including the hippocampus and lateral prefrontal cortex, as well as their affiliated default and frontoparietal control networks. Most studies have examined how structural or functional differences relate to memory independently. Here we implemented a multimodal, multivariate approach to investigate how interactions between individual differences in structural integrity and functional connectivity relate to episodic memory performance in healthy aging. In a sample of younger (N = 111; mean age, 22.11 years) and older (N = 78; mean age, 67.29 years) adults, we analyzed structural MRI and multiecho resting-state fMRI data. Participants completed measures of list recall (free recall of words from a list), associative memory (cued recall of paired words), and source memory (cued recall of the trial type, or the sensory modality in which a word was presented). The findings revealed that greater structural integrity of the posterior hippocampus and middle frontal gyrus were linked with a pattern of increased within-network connectivity, which together were related to better associative and source memory in older adulthood. Critically, older adults displayed better memory performance in the context of decreased hippocampal volumes when structural differences were accompanied by functional reorganization. This functional reorganization was characterized by a pruning of connections between the hippocampus and the limbic and frontoparietal control networks. Our work provides insight into the neural mechanisms that underlie age-related compensation, revealing that the functional architecture associated with better memory performance in healthy aging is tied to the structural integrity of the hippocampus and prefrontal cortex.

Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization

Sex plays a crucial role in human brain development, aging, and the manifestation of psychiatric and neurological disorders. However, our understanding of sex differences in human functional brain organization and their behavioral consequences has been hindered by inconsistent findings and a lack of replication. Here, we address these challenges using a spatiotemporal deep neural network (stDNN) model to uncover latent functional brain dynamics that distinguish male and female brains. Our stDNN model accurately differentiated male and female brains, demonstrating consistently high cross-validation accuracy (>90%), replicability, and generalizability across multisession data from the same individuals and three independent cohorts (N ~ 1,500 young adults aged 20 to 35). Explainable AI (XAI) analysis revealed that brain features associated with the default mode network, striatum, and limbic network consistently exhibited significant sex differences (effect sizes > 1.5) across sessions and independent cohorts. Furthermore, XAI-derived brain features accurately predicted sex-specific cognitive profiles, a finding that was also independently replicated. Our results demonstrate that sex differences in functional brain dynamics are not only highly replicable and generalizable but also behaviorally relevant, challenging the notion of a continuum in male-female brain organization. Our findings underscore the crucial role of sex as a biological determinant in human brain organization, have significant implications for developing personalized sex-specific biomarkers in psychiatric and neurological disorders, and provide innovative AI-based computational tools for future research.

Discriminative functional connectivity signature of cocaine use disorder links to rTMS treatment response.

Cocaine use disorder (CUD) is prevalent, and repetitive transcranial magnetic stimulation (rTMS) shows promise in reducing cravings. However, the association between a consistent CUD-specific functional connectivity signature and treatment response remains unclear. Here we identify a validated functional connectivity signature from functional magnetic resonance imaging to discriminate CUD, with successful independent replication. We found increased connectivity within the visual and dorsal attention networks and between the frontoparietal control and ventral attention networks, alongside reduced connectivity between the default mode and limbic networks in patients with CUD. These connections were associated with drug use history and cognitive impairments. Using data from a randomized clinical trial, we also established the prognostic value of these functional connectivities for rTMS treatment outcomes in CUD, especially involving the frontoparietal control and default mode networks. Our findings reveal insights into the neurobiological mechanisms of CUD and link functional connectivity biomarkers with rTMS treatment response, offering potential targets for future therapeutic development.

Intracranial Recordings of the Human Orbitofrontal Cortical Activity during Self-Referential Episodic and Valenced Self-Judgments.

We recorded directly from the orbital (oPFC) and ventromedial (vmPFC) subregions of the orbitofrontal cortex (OFC) in 22 (9 female, 13 male) epilepsy patients undergoing intracranial electroencephalography (iEEG) monitoring during an experimental task in which the participants judged the accuracy of self-referential autobiographical statements as well as valenced self-judgments (SJs). We found significantly increased high-frequency activity (HFA) in ∼13% of oPFC sites (10/18 subjects) and 16% of vmPFC sites (4/12 subjects) during both of these self-referential thought processes, with the HFA power being modulated by the content of self-referential stimuli. The location of these activated sites corresponded with the location of fMRI-identified limbic network. Furthermore, the onset of HFA in the vmPFC was significantly earlier than that in the oPFC in all patients with simultaneous recordings in both regions. In 11 patients with available depression scores from comprehensive neuropsychological assessments, we documented diminished HFA in the OFC during positive SJ trials among individuals with higher depression scores; responses during negative SJ trials were not related to the patients' depression scores. Our findings provide new temporal and anatomical information about the mode of engagement in two important subregions of the OFC during autobiographical memory and SJ conditions. Our findings from the OFC support the hypothesis that diminished brain activity during positive self-evaluations, rather than heightened activity during negative self-evaluations, plays a key role in the pathophysiology of depression.

The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan.

The hierarchical network architecture of the human brain, pivotal to cognition and behavior, can be explored via gradient analysis using restingstate functional MRI data. Although it has been employed to understand brain development and disorders, the impact of aging on this hierarchical architecture and its link to cognitive decline remains elusive. This study utilized resting-state functional MRI data from 350 healthy adults (aged 20-85) to investigate the functional hierarchical network using connectome gradient analysis with a cross-age sliding window approach. Gradient-related metrics were estimated and correlated with age to evaluate trajectory of gradient changes across lifespan. The principal gradient (unimodal-to-transmodal) demonstrated a significant non-linear relationship with age, whereas the secondary gradient (visual-to-somatomotor) showed a simple linear decreasing pattern. Among the principal gradient, significant age-related changes were observed in the somatomotor, dorsal attention, limbic and default mode networks. The changes in the gradient scores of both the somatomotor and frontal-parietal networks were associated with greater working memory and visuospatial ability. Gender differences were found in global gradient metrics and gradient scores of somatomotor and default mode networks in the principal gradient, with no interaction with age effect. Our study delves into the aging trajectories of functional connectome gradient and its cognitive impact across the adult lifespan, providing insights for future research into the biological underpinnings of brain function and pathological models of atypical aging processes.

Morphometric network-based abnormalities correlate with psychiatric comorbidities and gene expression in PCDH19-related developmental and epileptic encephalopathy

Protocadherin-19 (PCDH19) developmental and epileptic encephalopathy causes an early-onset epilepsy syndrome with limbic seizures, typically occurring in clusters and variably associated with intellectual disability and a range of psychiatric disorders including hyperactive, obsessive-compulsive and autistic features. Previous quantitative neuroimaging studies revealed abnormal cortical areas in the limbic formation (parahippocampal and fusiform gyri) and underlying white-matter fibers. In this study, we adopted morphometric, network-based and multivariate statistical methods to examine the cortex and substructure of the hippocampus and amygdala in a cohort of 20 PCDH19-mutated patients and evaluated the relation between structural patterns and clinical variables at individual level. We also correlated morphometric alterations with known patterns of PCDH19 expression levels. We found patients to exhibit high-significant reductions of cortical surface area at a whole-brain level (left/right pvalue = 0.045/0.084), and particularly in the regions of the limbic network (left/right parahippocampal gyri pvalue = 0.230/0.016; left/right entorhinal gyri pvalue = 0.002/0.327), and bilateral atrophy of several subunits of the amygdala and hippocampus, particularly in the CA regions (head of the left CA3 pvalue = 0.002; body of the right CA3 pvalue = 0.004), and differences in the shape of hippocampal structures. More severe psychiatric comorbidities correlated with more significant altered patterns, with the entorhinal gyrus (pvalue = 0.013) and body of hippocampus (pvalue = 0.048) being more severely affected. Morphometric alterations correlated significantly with the known expression patterns of PCDH19 (rvalue = -0.26, pspin = 0.092). PCDH19 encephalopathy represents a model of genetically determined neural network based neuropsychiatric disease in which quantitative MRI-based findings correlate with the severity of clinical manifestations and had have a potential predictive value if analyzed early.

BOLD signal variability as potential new biomarker of functional neurological disorders

BackgroundFunctional neurological disorder (FND) is a common neuropsychiatric condition with established diagnostic criteria and effective treatments but for which the underlying neuropathophysiological mechanisms remain incompletely understood. Recent neuroimaging studies have revealed FND as a multi-network brain disorder, unveiling alterations across limbic, self-agency, attentional/salience, and sensorimotor networks. However, the relationship between identified brain alterations and disease progression or improvement is less explored. MethodsThis study included resting-state functional magnetic resonance imaging (fMRI) data from 79 patients with FND and 74 age and sex-matched healthy controls (HC). First, voxel-wise BOLD signal variability was computed for each participant and the group-wise difference was calculated. Second, we investigated the potential of BOLD signal variability to serve as a prognostic biomarker for clinical outcome in 47 patients who attended a follow-up measurement after eight months. ResultsThe results demonstrated higher BOLD signal variability in key networks, including the somatomotor, salience, limbic, and dorsal attention networks, in patients compared to controls. Longitudinal analysis revealed an increase in BOLD signal variability in the supplementary motor area (SMA) in FND patients who had an improved clinical outcome, suggesting SMA variability as a potential state biomarker. Additionally, higher BOLD signal variability in the left insula at baseline predicted a worse clinical outcome. ConclusionThis study contributes to the understanding of FND pathophysiology, emphasizing the dynamic nature of neural activity and highlighting the potential of BOLD signal variability as a valuable research tool. The insula and SMA emerge as promising regions for further investigation as prognostic and state markers.

Right-to-left shunt-associated brain functional changes in migraine: evidences from a resting-state FMRI study.

Migraine, a neurological condition perpetually under investigation, remains shrouded in mystery regarding its underlying causes. While a potential link to Right-to-Left Shunt (RLS) has been postulated, the exact nature of this association remains elusive, necessitating further exploration. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo) and functional connectivity (FC) were employed to investigate functional segregation and functional integration across distinct brain regions. Graph theory-based network analysis was utilized to assess functional networks in migraine patients with RLS. Pearson correlation analysis further explored the relationship between RLS severity and various functional metrics. Compared with migraine patients without RLS, patients with RLS exhibited a significant increase in the ALFF within left middle occipital and superior occipital gyrus; In migraine patients with RLS, significantly reduced brain functional connectivity was found, including the connectivity between default mode network and visual network, ventral attention network, as well as the intra-functional connectivity of somatomotor network and its connection with the limbic network, and also the connectivity between the left rolandic operculum and the right middle cingulate gyrus. Notably, a significantly enhanced functional connectivity between the frontoparietal network and the ventral attention network was found in migraine with RLS; Patients with RLS displayed higher values of the normalized clustering coefficient and greater betweenness centrality in specific regions, including the left precuneus, right insula, and right inferior temporal gyrus. Additionally, these patients displayed a diminished nodal degree in the occipital lobe and reduced nodal efficiency within the fusiform gyrus; Further, the study found positive correlations between ALFF in the temporal lobes, thalamus, left middle occipital, and superior occipital gyrus and RLS severity. Conversely, negative correlations emerged between ALFF in the right inferior frontal gyrus, middle frontal gyrus, and insula and RLS grading. Finally, the study identified a positive correlation between angular gyrus betweenness centrality and RLS severity. RLS-associated brain functional alterations in migraine consisted of local brain regions, connectivity, and networks involved in pain conduction and regulation did exist in migraine with RLS.