A case for motor network contributions to schizophrenia symptoms: Evidence from resting‐state connectivity

Though schizophrenia (SCZ) is classically defined based on positive symptoms and the negative symptoms of the disease prove to be debilitating for many patients, motor deficits are often present as well. A growing literature highlights the importance of motor systems and networks in the disease, and it may be the case that dysfunction in motor networks relates to the pathophysiology and etiology of SCZ. To test this and build upon recent work in SCZ and in at‐risk populations, we investigated cortical and cerebellar motor functional networks at rest in SCZ and controls using publically available data. We analyzed data from 82 patients and 88 controls. We found key group differences in resting‐state connectivity patterns that highlight dysfunction in motor circuits and also implicate the thalamus. Furthermore, we demonstrated that in SCZ, these resting‐state networks are related to both positive and negative symptom severity. Though the ventral prefrontal cortex and corticostriatal pathways more broadly have been implicated in negative symptom severity, here we extend these findings to include motor–striatal connections, as increased connectivity between the primary motor cortex and basal ganglia was associated with more severe negative symptoms. Together, these findings implicate motor networks in the symptomatology of psychosis, and we speculate that these networks may be contributing to the etiology of the disease. Overt motor deficits in SCZ may signal underlying network dysfunction that contributes to the overall disease state. Hum Brain Mapp 38:4535–4545, 2017. © 2017 Wiley Periodicals, Inc.     

Machine learning identifies unaffected first‐degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients

Schizophrenia (SCZ) patients and their unaffected first‐degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large‐scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs—the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus—were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave‐one‐out cross‐validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.     

Network abnormalities among non‐manifesting Parkinson disease related LRRK2 mutation carriers

Non‐manifesting carriers (NMC) of the G2019S mutation in the LRRK2 gene represent an “at risk” group for future development of Parkinson's disease (PD) and have demonstrated task related fMRI changes. However, resting‐state networks have received less research focus, thus this study aimed to assess the integrity of the motor, default mode (DMN), salience (SAL), and dorsal attention (DAN) networks among this unique population by using two different connectivity measures: interregional functional connectivity analysis and Dependency network analysis (D_EPNA). Machine learning classification methods were used to distinguish connectivity between the two groups of participants. Forty‐four NMC and 41 non‐manifesting non‐carriers (NMNC) participated in this study; while no behavioral differences on standard questionnaires could be detected, NMC demonstrated lower connectivity measures in the DMN, SAL, and DAN compared to NMNC but not in the motor network. Significant correlations between NMC connectivity measures in the SAL and attention were identified. Machine learning classification separated NMC from NMNC with an accuracy rate above 0.8. Reduced integrity of non‐motor networks was detected among NMC of the G2019S mutation in the LRRK2 gene prior to identifiable changes in connectivity of the motor network, indicating significant non‐motor cerebral changes among populations “at risk” for future development of PD.     

Parkinson's disease patients show reduced cortical‐subcortical sensorimotor connectivity

Reduced dopamine input to cortical and subcortical brain structures, particularly those in the sensorimotor network, is a hallmark of Parkinson's disease (PD). The extent to which dopamine dysfunction affects connectivity within this and other brain networks remains to be investigated. The purpose of this study was to measure anatomical and functional connectivity in groups of PD patients and controls to determine whether connectivity deficits within the cortico–basal ganglia thalamocortical system could be attributed to PD, particularly in sensorimotor connections. A neuroimaging paradigm involving diffusion‐weighted magnetic resonance imaging (MRI) and resting‐state functional MRI was implemented in a large cohort of PD patients and control subjects. Probabilistic tractography and functional correlation analyses were performed to map connections between brain structures and to derive indices of connectivity that were then used to compare groups. Anatomical connectivity deficits were demonstrated in PD patients, specifically for sensorimotor connections. Functional deficits were also found in some of the same connections. In addition, functional connectivity was found to increase in associative and limbic connections in PD patients compared with controls. This study lends support to findings regarding the dysfunction of the sensorimotor circuit in PD. As deficits in anatomical and functional connectivity within this circuit were in some cases concordant in PD patients, a possible link between brain structure and function is suggested. Increases in functional connectivity in other cortico–basal ganglia thalamocortical circuits may be indicative of compensatory effects in response to system deficits elsewhere. © 2012 Movement Disorder Society     

Anomalous single‐subject based morphological cortical networks in drug‐naive,first‐episode major depressive disorder

Major depressive disorder (MDD) has been associated with disruptions in the topological organization of brain morphological networks in group‐level data. Such disruptions have not yet been identified in single‐patients, which is needed to show relations with symptom severity and to evaluate their potential as biomarkers for illness. To address this issue, we conducted a cross‐sectional structural brain network study of 33 treatment‐naive, first‐episode MDD patients and 33 age‐, gender‐, and education‐matched healthy controls (HCs). Weighted graph‐theory based network models were used to characterize the topological organization of brain networks between the two groups. Compared with HCs, MDD patients exhibited lower normalized global efficiency and higher modularity in their whole‐brain morphological networks, suggesting impaired integration and increased segregation of morphological brain networks in the patients. Locally, MDD patients exhibited lower efficiency in anatomic organization for transferring information predominantly in default‐mode regions including the hippocampus, parahippocampal gyrus, precuneus and superior parietal lobule, and higher efficiency in the insula, calcarine and posterior cingulate cortex, and in the cerebellum. Morphological connectivity comparisons revealed two subnetworks that exhibited higher connectivity strength in MDD mainly involving neocortex‐striatum‐thalamus‐cerebellum and thalamo‐hippocampal circuitry. MDD‐related alterations correlated with symptom severity and differentiated individuals with MDD from HCs with a sensitivity of 87.9% and specificity of 81.8%. Our findings indicate that single subject grey matter morphological networks are often disrupted in clinically relevant ways in treatment‐naive, first episode MDD patients. Circuit‐specific changes in brain anatomic network organization suggest alterations in the efficiency of information transfer within particular brain networks in MDD. Hum Brain Mapp 38:2482–2494, 2017. © 2017 Wiley Periodicals, Inc.     

Reduced neural connectivity but increased task‐related activity during working memory in de novo Parkinson patients

Objective: Patients with Parkinson's disease (PD) often suffer from impairments in executive functions, such as working memory deficits. It is widely held that dopamine depletion in the striatum contributes to these impairments through decreased activity and connectivity between task‐related brain networks. We investigated this hypothesis by studying task‐related network activity and connectivity within a sample of de novo patients with PD, versus healthy controls, during a visuospatial working memory task. Methods: Sixteen de novo PD patients and 35 matched healthy controls performed a visuospatial n‐back task while we measured their behavioral performance and neural activity using functional magnetic resonance imaging. We constructed regions‐of‐interest in the bilateral inferior parietal cortex (IPC), bilateral dorsolateral prefrontal cortex (DLPFC), and bilateral caudate nucleus to investigate group differences in task‐related activity. We studied network connectivity by assessing the functional connectivity of the bilateral DLPFC and by assessing effective connectivity within the frontoparietal and the frontostriatal networks. Results: PD patients, compared with controls, showed trend‐significantly decreased task accuracy, significantly increased task‐related activity in the left DLPFC and a trend‐significant increase in activity of the right DLPFC, left caudate nucleus, and left IPC. Furthermore, we found reduced functional connectivity of the DLPFC with other task‐related regions, such as the inferior and superior frontal gyri, in the PD group, and group differences in effective connectivity within the frontoparietal network. Interpretation: These findings suggest that the increase in working memory‐related brain activity in PD patients is compensatory to maintain behavioral performance in the presence of network deficits. Hum Brain Mapp 36:1554–1566, 2015. © 2015 Wiley Periodicals, Inc.     

Spatio‐temporal dynamics of resting‐state brain networks improve single‐subject prediction of schizophrenia diagnosis

Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding‐window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting‐state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting‐state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting‐state dynamics are not trivially attributable to sampling variability and head motion.     

Intra‐ and inter‐network functional alterations in Parkinson's disease with mild cognitive impairment

Mild cognitive impairment (MCI) is prevalent in 15%–40% of Parkinson's disease (PD) patients at diagnosis. In this investigation, we study brain intra‐ and inter‐network alterations in resting state functional magnetic resonance imaging (rs‐fMRI) in recently diagnosed PD patients and characterise them as either cognitive normal (PD‐NC) or with MCI (PD‐MCI). Patients were divided into two groups, PD‐NC (N = 62) and PD‐MCI (N = 37) and for comparison, healthy controls (HC, N = 30) were also included. Intra‐ and inter‐network connectivity were investigated from participants’ rs‐fMRIs in 26 resting state networks (RSNs). Intra‐network differences were found between both patient groups and HCs for networks associated with motor control (motor cortex), spatial attention and visual perception. When comparing both PD‐NC and PD‐MCI, intra‐network alterations were found in RSNs related to attention, executive function and motor control (cerebellum). The inter‐network analysis revealed a hyper‐synchronisation between the basal ganglia network and the motor cortex in PD‐NC compared with HCs. When both patient groups were compared, intra‐network alterations in RSNs related to attention, motor control, visual perception and executive function were found. We also detected disease‐driven negative synchronisations and synchronisation shifts from positive to negative and vice versa in both patient groups compared with HCs. The hyper‐synchronisation between basal ganglia and motor cortical RSNs in PD and its synchronisation shift from negative to positive compared with HCs, suggest a compensatory response to basal dysfunction and altered basal‐cortical motor control in the resting state brain of PD patients. Hum Brain Mapp 38:1702–1715, 2017 . © 2016 Wiley Periodicals, Inc.     

Cognitive impairment and resting‐state network connectivity in Parkinson's disease

The purpose of this work was to evaluate changes in the connectivity patterns of a set of cognitively relevant, dynamically interrelated brain networks in association with cognitive deficits in Parkinson's disease (PD) using resting‐state functional MRI. Sixty‐five nondemented PD patients and 36 matched healthy controls were included. Thirty‐four percent of PD patients were classified as having mild cognitive impairment (MCI) based on performance in attention/executive, visuospatial/visuoperceptual (VS/VP) and memory functions. A data‐driven approach using independent component analysis (ICA) was used to identify the default‐mode network (DMN), the dorsal attention network (DAN) and the bilateral frontoparietal networks (FPN), which were compared between groups using a dual‐regression approach controlling for gray matter atrophy. Additional seed‐based analyses using a priori defined regions of interest were used to characterize local changes in intranetwork and internetwork connectivity. Structural group comparisons through voxel‐based morphometry and cortical thickness were additionally performed to assess associated gray matter atrophy. ICA results revealed reduced connectivity between the DAN and right frontoinsular regions in MCI patients, associated with worse performance in attention/executive functions. The DMN displayed increased connectivity with medial and lateral occipito‐parietal regions in MCI patients, associated with worse VS/VP performance, and with occipital reductions in cortical thickness. In line with data‐driven results, seed‐based analyses mainly revealed reduced within‐DAN, within‐DMN and DAN‐FPN connectivity, as well as loss of normal DAN‐DMN anticorrelation in MCI patients. Our findings demonstrate differential connectivity changes affecting the networks evaluated, which we hypothesize to be related to the pathophysiological bases of different types of cognitive impairment in PD. Hum Brain Mapp, 36:199–212, 2015. © 2014 Wiley Periodicals, Inc.     

Intrinsic brain connectivity predicts impulse control disorders in patients with Parkinson's disease

Background: Impulse control disorders can be triggered by dopamine replacement therapies in patients with PD. Using resting‐state functional MRI, we investigated the intrinsic brain network connectivity at baseline in a cohort of drug‐naive PD patients who successively developed impulse control disorders over a 36‐month follow‐up period compared with patients who did not. Methods: Baseline 3‐Tesla MRI images of 30 drug‐naive PD patients and 20 matched healthy controls were analyzed. The impulse control disorders' presence and severity at follow‐up were assessed by the Questionnaire for Impulsive‐Compulsive Disorders in Parkinson's Disease Rating Scale. Single‐subject and group‐level independent component analysis was used to investigate functional connectivity differences within the major resting‐state networks. We also compared internetwork connectivity between patients. Finally, a multivariate Cox regression model was used to investigate baseline predictors of impulse control disorder development. Results: At baseline, decreased connectivity in the default‐mode and right central executive networks and increased connectivity in the salience network were detected in PD patients with impulse control disorders at follow‐up compared with those without. Increased default‐mode/central executive internetwork connectivity was significantly associated with impulse control disorders development (P < 0.05). Conclusions: Our findings demonstrated that abnormal brain connectivity in the three large‐scale networks characterizes drug‐naive PD patients who will eventually develop impulse control disorders while on dopaminergic treatment. We hypothesize that these divergent cognitive and limbic network connectivity changes could represent a potential biomarker and an additional risk factor for the emergence of impulse control disorders. © 2017 International Parkinson and Movement Disorder Society     

Gender differences in healthy aging and Alzheimer's Dementia: A 18F‐FDG‐PET study of brain and cognitive reserve

Cognitive reserve (CR) and brain reserve (BR) are protective factors against age‐associated cognitive decline and neurodegenerative disorders. Very limited evidence exists about gender effects on brain aging and on the effect of CR on brain modulation in healthy aging and Alzheimer's Dementia (AD). We investigated gender differences in brain metabolic activity and resting‐state network connectivity, as measured by ¹⁸F‐FDG‐PET, in healthy aging and AD, also considering the effects of education and occupation. The clinical and imaging data were retrieved from large datasets of healthy elderly subjects (HE) (225) and AD patients (282). In HE, males showed more extended age‐related reduction of brain metabolism than females in frontal medial cortex. We also found differences in brain modulation as metabolic increases induced by education and occupation, namely in posterior associative cortices in HE males and in the anterior limbic‐affective and executive networks in HE females. In AD patients, the correlations between education and occupation levels and brain hypometabolism showed gender differences, namely a posterior temporo‐parietal association in males and a frontal and limbic association in females, indicating the involvement of different networks. Finally, the metabolic connectivity in both HE and AD aligned with these results, suggesting greater efficiency in the posterior default mode network for males, and in the anterior frontal executive network for females. The basis of these brain gender differences in both aging and AD, obtained exploring cerebral metabolism, metabolic connectivity and the effects of education and occupation, is likely at the intersection between biological and sociodemographic factors. Hum Brain Mapp 38:4212–4227, 2017. © 2017 Wiley Periodicals, Inc.     

Reduced specificity of functional connectivity in the aging brain during task performance

The importance of studying connectivity in the aging brain is increasingly recognized. Recent studies have shown that connectivity within the default mode network is reduced with age and have demonstrated a clear relation of these changes with cognitive functioning. However, research on age‐related changes in other functional networks is sparse and mainly focused on prespecified functional networks. Using functional magnetic resonance imaging, we investigated age‐related changes in functional connectivity during a visual oddball task in a range of functional networks. It was found that compared with young participants, elderly showed a decrease in connectivity between areas belonging to the same functional network. This was found in the default mode network and the somatomotor network. Moreover, in all identified networks, elderly showed increased connectivity between areas within these networks and areas belonging to different functional networks. Decreased connectivity within functional networks was related to poorer cognitive functioning in elderly. The results were interpreted as a decrease in the specificity of functional networks in older participants. Hum Brain Mapp 35:319–330, 2014. © 2012 Wiley Periodicals, Inc.     

Analysis of structure–function network decoupling in the brain systems of spastic diplegic cerebral palsy

Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure–function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting‐state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole‐brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural–functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure–function coupling than the control group. This reduced coupling but reverse directionality in the whole‐brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation‐based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure–function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292–5306, 2017. © 2017 Wiley Periodicals, Inc.     

The same,but different: Preserved distractor suppression in old age is implemented through an age‐specific reactive ventral fronto‐parietal network

Previous studies have shown age‐related impairments in the ability to suppress salient distractors. One possibility is that this is mediated by age‐related impairments in the recruitment of the left intraparietal sulcus (Left IPS), which has been shown to mediate the suppression of salient distractors in healthy, young participants. Alternatively, this effect may be due to a shift in engagement from proactive control to reactive control, possibly to compensate for age‐related impairments in proactive control. Another possibility is that this is due to changes in the functional specificity of brain regions that mediate salience suppression, expressed in changes in spontaneous connectivity of these regions. We assessed these possibilities by having participants engage in a proactive distractor suppression task while in an fMRI scanner. Although we did not find any age‐related differences in behavior, the young (N = 15) and older (N = 15) cohorts engaged qualitatively distinctive brain networks to complete the task. Younger participants engaged the predicted proactive control network, including the Left IPS. On the other hand, older participants simultaneously engaged both a proactive and a reactive network, but this was not a consequence of reduced network specificity as resting state functional connectivity was largely comparable in both age groups. Furthermore, improved behavioral performance for older adults was associated with increased resting state functional connectivity between these two networks. Overall, the results of this study suggest that age‐related differences in the recruitment of a left lateralized ventral fronto‐parietal network likely reflect the specific recruitment of reactive control mechanisms for distractor inhibition.     

Functional connectivity in dementia with Lewy bodies: A within‐ and between‐network analysis

Dementia with Lewy bodies (DLB) is a common form of dementia and is characterized by cognitive fluctuations, visual hallucinations, and Parkinsonism. The phenotypic expression of the disease may, in part, relate to alterations in functional connectivity within and between brain networks. This resting‐state study sought to clarify this in DLB, how networks differed from Alzheimer's disease (AD), and whether they were related to clinical symptoms in DLB. Resting‐state networks were estimated using independent component analysis. We investigated functional connectivity changes in 31 DLB patients compared to 31 healthy controls and a disease comparator group of 29 AD patients using dual regression and FSLNets. Within‐network connectivity was generally decreased in DLB compared to controls, mainly in motor, temporal, and frontal networks. Between‐network connectivity was mainly intact; only the connection between a frontal and a temporal network showed increased connectivity in DLB. Differences between AD and DLB were subtle and we did not find any significant correlations with the severity of clinical symptoms in DLB. This study emphasizes the importance of reduced connectivity within motor, frontal, and temporal networks in DLB with relative sparing of the default mode network. The lack of significant correlations between connectivity measures and clinical scores indicates that the observed reduced connectivity within these networks might be related to the presence, but not to the severity of motor and cognitive impairment in DLB patients. Furthermore, our results suggest that AD and DLB may show more similarities than differences in patients with mild disease.     

Altered resting‐state connectivity in Huntington's Disease

Huntington's disease (HD) is an autosomal dominantly inherited neurodegenerative disorder characterized by motor, cognitive, and psychiatric symptoms. Using resting‐state fMRI (rs‐fMRI) we investigated the functional integrity of resting‐state networks (RSN) in HD. 17 HD and 19 matched control participants were examined at a 3 Tesla MR scanner. After controlling for structural degeneration by means of voxel‐based morphometry, task‐free rs‐fMRI data were analyzed using Independent Component Analysis (ICA) and a dual‐regression approach in the context of genetic and clinical parameters. Further, we evaluated HD‐related differences in interregional connectivity between networks. RSN analysis showed a significant increase in intrinsic functional connectivity in the HD sample compared with controls, including the thalamus, striatum, prefrontal, premotor, and parietal maps. A subset of the Default Mode Network (DMN) was also affected. In the HD cohort, motor impairment correlated with higher network connectivity in mainly motor and parietal cortices. Deteriorating total functional capacity was additionally associated with higher connectivity in the striatum, thalamus, insular and frontal areas. This pattern of increased activity in intrinsic functional networks might suggest a reduced ability of intra‐network differentiation with clinical disease progression in HD. Finally, results showed reduced long‐range connectivity between parietal ICA components in HD compared to controls, indicating impaired functional coupling between interregional networks in HD. Our data demonstrates that functional connectivity is profoundly altered in HD, both within and between RSN. Rs‐fMRI analysis may provide additional valuable insights into neuronal dysfunctions beyond HD‐related structural degeneration and disruptions of functional circuits in HD. Hum Brain Mapp 35:2582–2593, 2014. © 2013 Wiley Periodicals, Inc .     

Connectivity strength‐weighted sparse group representation‐based brain network construction for MCI classification

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l₁‐norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength‐weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting‐state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370–2383, 2017. © 2017 Wiley Periodicals, Inc.     

Meta‐analytic evidence for altered mesolimbic responses to reward in schizophrenia

Dysfunction of reward‐related neural circuitry in schizophrenia (SCZ) has been widely reported, and may provide insight into the motivational and cognitive disturbances that characterize the disorder. Although previous meta‐analyses of reward learning paradigms in SCZ have been performed, a meta‐analysis of whole‐brain coordinate maps in SCZ alone has not been conducted. In this study, we performed an activation likelihood estimate (ALE) meta‐analysis, and performed a follow‐up analysis of functional connectivity and functional decoding of identified regions. We report several salient findings that extend prior work in this area. First, an alteration in reward‐related activation was observed in the right ventral striatum, but this was not solely driven by hypoactivation in the SCZ group compared to healthy controls. Second, the region was characterized by functional connectivity primarily with the lateral prefrontal cortex and pre‐supplementary motor area (preSMA), as well as subcortical regions such as the thalamus which show structural deficits in SCZ. Finally, although the meta‐analysis showed no regions outside the ventral striatum to be significantly altered, regions with higher functional connectivity with the ventral striatum showed a greater number of subthreshold foci. Together, these findings confirm the alteration of ventral striatal function in SCZ, but suggest that a network‐based approach may assist future analysis of the functional underpinnings of the disorder.     

Disruption of structural and functional networks in long‐standing multiple sclerosis

Both gray matter atrophy and disruption of functional networks are important predictors for physical disability and cognitive impairment in multiple sclerosis (MS), yet their relationship is poorly understood. Graph theory provides a modality invariant framework to analyze patterns of gray matter morphology and functional coactivation. We investigated, how gray matter and functional networks were affected within the same MS sample and examined their interrelationship. Magnetic resonance imaging and magnetoencephalography (MEG) were performed in 102 MS patients and 42 healthy controls. Gray matter networks were computed at the group‐level based on cortical thickness correlations between 78 regions across subjects. MEG functional networks were computed at the subject level based on the phase‐lag index between time‐series of regions in source‐space. In MS patients, we found a more regular network organization for structural covariance networks and for functional networks in the theta band, whereas we found a more random network organization for functional networks in the alpha2 band. Correlation analysis revealed a positive association between covariation in thickness and functional connectivity in especially the theta band in MS patients, and these results could not be explained by simple regional gray matter thickness measurements. This study is a first multimodal graph analysis in a sample of MS patients, and our results suggest that a disruption of gray matter network topology is important to understand alterations in functional connectivity in MS as regional gray matter fails to take into account the inherent connectivity structure of the brain. Hum Brain Mapp 35:5946–5961, 2014. © 2014 Wiley Periodicals, Inc.     

Modular‐level alterations of structure–function coupling in schizophrenia connectome

Convergent evidences have revealed that schizophrenia is associated with brain dysconnectivity, which leads to abnormal network organization. However, discrepancies were apparent between the structural connectivity (SC) and functional connectivity (FC) studies, and the relationship between structural and functional deficits in schizophrenia remains largely unknown. In this study, resting‐state functional magnetic resonance imaging and structural diffusion tensor imaging were performed in 20 patients with schizophrenia and 20 matched healthy volunteers (patients/controls = 19/17 after head motion rejection). Functional and structural brain networks were obtained for each participant. Graph theoretical approaches were employed to parcellate the FC networks into functional modules. The relationships between the entries of SC and FC were estimated within each module to identify group differences and their correlations with clinical symptoms. Although five common functional modules (including the default mode, occipital, subcortical, frontoparietal, and central modules) were identified in both groups, the patients showed a significantly reduced modularity in comparison with healthy participants. Furthermore, we found that schizophrenia‐related aberrations of SC–FC coupling exhibited complex patterns among modules. Compared with controls, patients showed an increased SC–FC coupling in the default mode and the central modules. Moreover, significant SC–FC decoupling was demonstrated in the occipital and the subcortical modules, which was associated with longer duration of illness and more severe clinical manifestations of schizophrenia. Taken together, these findings demonstrated that altered module‐dependent SC–FC coupling may underlie abnormal brain function and clinical symptoms observed in schizophrenia and highlighted the potential for using new multimodal neuroimaging biomarkers for diagnosis and severity evaluation of schizophrenia. Hum Brain Mapp 38:2008–2025, 2017. © 2017 Wiley Periodicals, Inc.