Enhanced diversity on connector hubs following sleep deprivation: Evidence from diffusion and functional magnetic resonance imaging.

Sleep deprivation has been demonstrated to exert widespread and intricate impacts on the brain network. The human brain network is a modular network composed of interconnected nodes. This network consists of provincial hubs and connector hubs, with provincial hubs having diverse connectivities within their own modules, while connector hubs distribute their connectivities across different modules. The latter is crucial for integrating information from various modules and ensuring the normal functioning of the modular brain. However, there has been a lack of systematic investigation into the impact of sleep deprivation on brain connector hubs. In this study, we utilized functional connectivity from resting-state functional magnetic resonance imaging, as well as structural connectivity from diffusion-weighted imaging, to systematically explore the variation of connector hub properties in the cerebral cortex after one night of sleep deprivation. The normalized participation coefficients (PCnorm) were utilized to identify connector hubs. In both the functional and structural networks, connector hubs exhibited a significant increase in average PCnorm, indicating the diversity enhancement of the connector hub following sleep deprivation. This enhancement is associated with increased network cost, reduced modularity, and decreased small-worldness, but enhanced global efficiency. This may potentially signify a compensatory mechanism within the brain following sleep deprivation. The significantly affected connector hubs were primarily observed in both the Control Network and Salience Network. We believe that the observed results reflect the increasing demand on the brain to invest more effort at preventing performance deterioration after sleep loss, in exchange for increased communication efficiency, especially involving systems responsible for neural resource allocation and cognitive control. These results have been replicated in an independent dataset. In conclusion, this study has enhanced our understanding of the compensatory mechanism in the brain response to sleep deprivation. This compensation is characterized by an enhancement in the connector hubs responsible for inter-modular communication, especially those related to neural resource and cognitive control. As a result, this compensation comes with a higher network cost but leads to an improvement in global communication efficiency, akin to a more random-like network manner.

Self-Therapeutic Nanoparticle That Alters Tau Protein and Ameliorates Tauopathy Toward a Functional Nanomedicine to Tackle Alzheimer's.

Tauopathy is a complex disorder associated at the junction of several other pathologies. Intrinsically disordered tau protein remains therapeutically challenging due to its undruggable nature and is a possible reason for monumental failure of several tau-based therapies. Herein, nanogold remodeled tau is reported as a pseudo-nanochaperon and shows therapeutic benefit by passive targeting in transgenic tau P301L mutant mice. Treatment with nanogold polyethylene glycol (Au-PEG) conjugate moderately improves the learning ability of the tau P301L mice that corroborates with diminished phosphorylated tau burden. Circulating total tau level that acts in a prion fashion is significantly reduced upon Au-PEG treatment. Similarly, a high level of tau is found in macaque monkey serum and Au-PEG inhibits amyloidosis of Alzheimer's patients and primate's serum samples ex vivo. Addtionally, brain MRI of an old aged macaque monkey shows the decrease of grey matter, which correlates with mutual loss of grey matter upon progressive dementia as reported. Au-PEG tunes tau and other circulating pro-dementia factors that are present in human AD serum, by remodeling the protein and repairing aberrant proteostasis. Alteration of proteotoxic tau function by nanogold as a kinetic stablizer holds translational potential to combat socially challenging dementia.

Functional dysconnectivity within the emotion-regulating system is associated with affective symptoms in major depressive disorder: A resting-state fMRI study.

OBJECTIVE: Major depressive disorder (MDD) can be characterized as a multidimensional and system-level disorder. The neuropathophysiological abnormalities have been reported to be distributed in emotion regulation system, involving the prefrontal cortex (PFC), limbic and striatum in convergent studies. Decrease of positive affect and increase of negative affect are recognized as a hallmark of MDD. However, the dysfunctions in affective processing in MDD within the emotion regulation system remains largely unclear. In this study, our goals are to characterize the dysconnectivity pattern within this system and explore the relationships between this kind of dysconnectivity pattern and affective symptoms, which might help us better look into the neuropathophysiological mechanisms underlying MDD. METHODS: A total of 34 MDD and 34 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rsfMRI). The alterations in functional connectivity (FC) within the emotion regulation system and their relationships with affective symptoms were explored. RESULTS: Compared with HCs, MDD patients showed aberrant FC within this system. Importantly, deceased FC was mainly involved in the prefrontal-limbic system, while elevated FC was observed in the prefrontal-striatum system. In the MDD group, decreased FC of right posterior hippocampus-left dorsolateral prefrontal cortex (dlPFC) was negatively associated with the negative affect scores and Hamilton Depression Rating Scale scores and the FC of left ventral striatum-left dlPFC was significantly negatively related with the positive affect scores. CONCLUSIONS: These findings demonstrated that MDD showed characteristic pathological alterations of the emotion regulation system. Dysconnectivity within prefrontal-limbic system might be more related to the dysregulation of negative affect, whereas dysconnectivity within prefrontal-striatum system might influence more on positive affect processing. The decrease in positive affect and increase in negative affect in MDD might have different pathological basis. These results could help better understand the dysconnectivity pattern in the emotion-regulating system underlying depression.

Thalamocortical dysconnectivity in paroxysmal kinesigenic dyskinesia: Combining functional magnetic resonance imaging and diffusion tensor imaging.

BACKGROUND: Paroxysmal kinesigenic dyskinesia is associated with macrostructural and microstructural abnormalities in the thalamus. OBJECTIVES: To examine functional and structural connectivity of thalamocortical networks in paroxysmal kinesigenic dyskinesia and to further investigate the effect of mutation of the proline-rich transmembrane protein 2 on thalamocortical networks. METHODS: Patients with paroxysmal kinesigenic dyskinesia (n = 20), subdivided into proline-rich transmembrane protein 2-mutated (n = 8) and nonmutated patients (n = 12) and healthy controls (n = 20) underwent resting-state functional MRI and diffusion imaging scan. The functional properties of correlations in neural activity (functional connectivity) and the structural properties of white matter probabilistic tractography (structural connectivity) were analyzed to characterize thalamocortical networks. Furthermore, the effect of proline-rich transmembrane protein 2 mutation on functional and structural connectivity of thalamocortical networks were examined using one-way analysis of variance among three groups. RESULTS: Patients had increased functional and structural connectivity between ventral lateral/anterior thalamic nuclei and a lateral motor area, as compared to controls. This functional connectivity positively correlated with disease duration. Interestingly, proline-rich transmembrane protein 2-mutated patients showed decreased functional connectivity and preserved structural connectivity, between mediodorsal nucleus and prefrontal cortex, compared to nonmutated patients and controls. CONCLUSIONS: Thalamomotor/premotor hyperconnectivity suggests abnormal communication between thalamus and motor cortex in patients. Furthermore, thalamoprefrontal hypoconnectivity in proline-rich transmembrane protein 2-mutated patients might indicate that proline-rich transmembrane protein 2 mutations result in inefficient thalamoprefrontal integration. Our findings facilitate a deeper understanding of the crucial role of thalamocortical dysconnectivity in the pathophysiological mechanisms of paroxysmal kinesigenic dyskinesia. © 2017 International Parkinson and Movement Disorder Society.

Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities.

Frequency-specific alterations in the fractional amplitude of low-frequency fluctuations in amyotrophic lateral sclerosis.

This study used resting-state functional magnetic resonance imaging and fractional amplitude of low-frequency fluctuations (fALFF) method to investigate low-frequency spontaneous neural activity at the bands of slow-5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz) in 20 patients with amyotrophic lateral sclerosis (ALS) and 20 healthy controls. We determined that, at slow-5 band, patients with ALS showed increased fALFF in the right middle frontal gyrus and decreased fALFF in the left middle occipital gyrus. However, compared with healthy controls, patients with ALS exhibited higher fALFF in the right caudate nucleus, left superior frontal gyrus, and right anterior cingulate cortex and lower fALFF in the right inferior occipital gyrus and bilateral middle occipital gyrus at slow-4 band. Furthermore, the fALFF value in the left superior frontal gyrus at slow-4 band was negatively correlated with functional rating scale-revised score. Our results demonstrated that the fALFF changes in ALS were widespread and frequency dependent. These findings may provide a novel way to look into the pathophysiology mechanisms underlying ALS.

Steady-state BOLD Response to Higher-order Cognition Modulates Low-Frequency Neural Oscillations.

Steady-state responses (SSRs) reflect the synchronous neural oscillations evoked by noninvasive and consistently repeated stimuli at the fundamental or harmonic frequencies. The steady-state evoked potentials (SSEPs; the representative form of the SSRs) have been widely used in the cognitive and clinical neurosciences and brain-computer interface research. However, the steady-state evoked potentials have limitations in examining high-frequency neural oscillations and basic cognition. In addition, synchronous neural oscillations in the low frequency range (<1 Hz) and in higher-order cognition have received a little attention. Therefore, we examined the SSRs in the low frequency range using a new index, the steady-state BOLD responses (SSBRs) evoked by semantic stimuli. Our results revealed that the significant SSBRs were induced at the fundamental frequency of stimuli and the first harmonic in task-related regions, suggesting the enhanced variability of neural oscillations entrained by exogenous stimuli. The SSBRs were independent of neurovascular coupling and characterized by sensorimotor bias, an indication of regional-dependent neuroplasticity. Furthermore, the amplitude of SSBRs may predict behavioral performance and show the psychophysiological relevance. Our findings provide valuable insights into the understanding of the SSRs evoked by higher-order cognition and how the SSRs modulate low-frequency neural oscillations.

Characterization of post-traumatic stress disorder using resting-state fMRI with a multi-level parametric classification approach.

Functional neuroimaging studies have found intra-regional activity and inter-regional connectivity alterations in patients with post-traumatic stress disorder (PTSD). However, the results of these studies are based on group-level statistics and therefore it is unclear whether PTSD can be discriminated at single-subject level, for instance using the machine learning approach. Here, we proposed a novel framework to identify PTSD using multi-level measures derived from resting-state functional MRI (fMRI). Specifically, three levels of measures were extracted as classification features: (1) regional amplitude of low-frequency fluctuations (univariate feature), which represents local spontaneous synchronous neural activity; (2) temporal functional connectivity (bivariate feature), which represents the extent of similarity of local activity between two regions, and (3) spatial functional connectivity (multivariate feature), which represents the extent of similarity of temporal correlation maps between two regions. Our method was evaluated on 20 PTSD patients and 20 demographically matched healthy controls. The experimental results showed that the features of each level could successfully discriminate PTSD patients from healthy controls. Furthermore, the combination of multi-level features using multi-kernel learning can further improve the classification performance. Specifically, the classification accuracy obtained by the proposed framework was 92.5 %, which was an increase of at least 5 and 17.5 % from the two-level and single-level feature based methods, respectively. Particularly, the limbic structure and prefrontal cortex provided the most discriminant features for classification, consistent with results reported in previous studies. Together, this study demonstrated for the first time that patients with PTSD can be identified at the individual level using resting-state fMRI data. The promising classification results indicated that this method may provide a complementary approach for improving the clinical diagnosis of PTSD.

Individual resting-state network functional connectivity predicts treatment improvement of repetitive transcranial magnetic stimulation in major depressive disorder: A pilot study.

The current pilot study aimed to exploratively investigate whether individual functional connectivity (FC) of the rTMS stimulation site with resting-state networks could predict the individual efficacy of rTMS treatment. We found that rTMS induced an increase of the FC between the stimulation site and the limbic network (LN) in healthy participants, and that this individualized FC was negatively correlated with the rTMS treatment improvement in MDD patients. Moreover, the LN successfully guided the personalized rTMS therapy. These findings highlighted the crucial role of the LN in understanding the mechanisms underlying rTMS treatment improvement, and the personalized therapy in MDD patients.

Brain structural changes underlying clinical symptom improvement following fast-acting treatments in treatment resistant depression.

BACKGROUND: Electroconvulsive therapy (ECT), ketamine infusion (KI), and total sleep deprivation (TSD) are effective and fast in treating patients with treatment-resistant depression (TRD). However, it remains unclear whether the three treatments have the same effect on clinical symptom improvement and have common brain structural mechanisms. METHODS: The current study included 127 TRD patients and 37 healthy controls, which were obtained from the Perturbation of the Treatment Resistant Depression Connectome Project. We aimed to investigate the shared and distinct brain structural changes underlying clinical symptom improvement among ECT, KI, and TSD treatments. RESULTS: All of the three treatments significantly reduced the depressive symptoms in TRD patients, but they differently affected other clinical measurements. Neuroimaging results also revealed that all of ECT, KI, and TSD treatments significantly increased gray matter volume of left caudate after treatment in TRD patients. However, the gray matter volume of other brain regions including hippocampus, parahippocampus, amygdala, insula, fusiform gyrus, several occipital and temporal areas was increased only after ECT treatment. Still, the baseline or the change of gray matter volume did not correlate with the depressive symptom improvement for all of the three treatments. LIMITATIONS: A higher sample size would be required to further validate our findings. CONCLUSIONS: The results observed in the current study suggested that the ECT, KI, and TSD treatments differently affected clinical measurements and brain structures in TRD patients, though all of them were effective in depressive symptom improvement, which might facilitate the development of personalized treatment protocol for this disease.

Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes.

BACKGROUND: Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied. METHODS: Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes. RESULTS: We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies. CONCLUSIONS: Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.

Volume of the Dentate Gyrus/CA4 Hippocampal subfield mediates the interplay between sleep quality and depressive symptoms.

Background: Emerging evidence increasingly suggests that poor sleep quality is associated with depressive symptoms. The hippocampus might play a crucial role in the interplay between sleep disturbance and depressive symptomatology, e.g., hippocampal atrophy is typically seen in both insomnia disorder and depression. Thus, examining the role of hippocampal volume in the interplay between poor sleep quality and depressive symptoms in large healthy populations is vital. Methods: We investigated the association between self-reported sleep quality, depressive symptoms, and hippocampal total and subfields' volumes in 1603 healthy young adults from the Behavioral Brain Research Project. Mediation analysis explored the mediating role of hippocampal volumes between sleep quality and depressive symptoms. Results: Self-reported sleep quality and depressive symptoms were positively correlated. In addition, it negatively related to three hippocampal subfields but not total hippocampal volume. In particular, hippocampal subfield DG and CA4 volumes mediated the interrelationship between poor sleep quality and depressive symptoms. Conclusions: Our findings improved the current understanding of the relationship between sleep disturbance, depressive symptomatology, and hippocampal subfields in healthy populations. Considering the crucial role of DG in hippocampal neurogenesis, our results suggest that poor sleep quality may contribute to depression through a reduction of DG volume leading to impaired neurogenesis which is crucial for the regulation of mood.

Enhanced rich club connectivity in mild or moderate depression after nonpharmacological treatment: A preliminary study.

INTRODUCTION: It has been suggested that the rich club organization in major depressive disorder (MDD) was altered. However, it remained unclear whether the rich club organization could be served as a biomarker that predicted the improvement of clinical symptoms in MDD. METHODS: The current study included 29 mild or moderate patients with MDD, who were grouped into a treatment group (receiving cognitive behavioral therapy or real-time fMRI feedback treatment) and a no-treatment group. Resting-state MRI scans were obtained for all participants. Graph theory was employed to investigate the treatment-related changes in network properties and rich club organization. RESULTS: We found that patients in the treatment group had decreased depressive symptom scores and enhanced rich club connectivity following the nonpharmacological treatment. Moreover, the changes in rich club connectivity were significantly correlated with the changes in depressive symptom scores. In addition, the nonpharmacological treatment on patients with MDD increased functional connectivity mainly among the salience network, default mode network, frontoparietal network, and subcortical network. Patients in the no-treatment group did not show significant changes in depressive symptom scores and rich club organization. CONCLUSIONS: Those results suggested that the remission of depressive symptoms after nonpharmacological treatment in MDD patients was associated with the increased efficiency of global information processing.

Electroencephalographic oscillations of alpha and beta rhythms during phrase-guessing procedure.

Phrases-guessing is one of the essential reasoning abilities in problem solving for human beings. However, it is still an open question about why individuals perform differently during the same reasoning task. In this study, we utilized a bilingual phrase-guessing task to explore the neural activities under the individually different performances with electroencephalography. Participants who had no knowledge of Greek were required to guess the meaning of a Greek phrase (long or short in length) by making an either-or selection as to which translation-equivalent Chinese word corresponds to Greek word. Names of color were used as experimental stimuli for which two Chinese words denoted the same color with one as a conventional color name and the other as a novel color name. The experiment yielded length of phrases (long vs. short) and novelty of phrases (novel vs. conventional) as variables. The behavioral results revealed significant length-by-novelty interaction on the number of selections. However, neither main effects nor interactive effects were found on response time. Further, the amplitude spectrums of high alpha rhythm, low alpha rhythm, and low beta rhythm during the task were positively associated with the participants' number of selections for a long Greek phrase with a novel and complex Chinese phrase (LNc) and a short Greek phrase with a conventional Chinese phrase (SCo), while negatively correlated with the response time of selections for LNc and SCo. Our findings suggested that the consistency between participants' behavior and electrophysiological oscillations (alpha and beta bands) could be employed as biomarkers for decoding the phrase-guessing procedure.

Stress and the brain: Emotional support mediates the association between myelination in the right supramarginal gyrus and perceived chronic stress.

Perceived stress, which refers to people's evaluation of a stressful event and their ability to cope with it, has emerged as a stable predictor for physical and mental health outcomes. Increasing evidence has suggested the buffering effect of social support on perceived stress. Although previous studies have investigated the brain structural features (e.g., gray matter volume) associated with perceived stress, less is known about the association between perceived chronic stress and intra-cortical myelin (ICM), which is an important microstructure of brain and is essential for healthy brain functions, and the role of social support in this association. Using a sample of 1076 healthy young adults drawn from the Human Connectome Project, we quantified the ICMby the contrast of T1w and T2w images and examined its association with perceived chronic stress during the last month and social support. Behavioral results showed that perceived chronic stress was negatively associated with both emotional support and instrumental support. Vertex-wise multiple regression analyses revealed that higher level of perceived chronic stress was significantly associated with lower ICM content of a cluster in the right supramarginal gyrus (rSMG). Interestingly, the emotional support, but not the instrumental support, significantly mediated the association of perceived chronic stress with ICM in the rSMG. Overall, the present study provides novel evidence for the cortical myelination of perceived chronic stress in humans and highlights the essential role of the rSMG in perceived chronic stress and emotional support.

Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality.

INTRODUCTION: Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear. METHODS: We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset. RESULTS: SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control. CONCLUSION: Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.

Sex differences of brain cortical structure in major depressive disorder.

Background: Major depressive disorder (MDD) has different clinical presentations in males and females. However, the neuroanatomical mechanisms underlying these sex differences are not fully understood. Objective: The purpose of present study was to explore the sex differences in brain cortical thickness (CT) and surface area (SA) of MDD and the relationship between these differences and clinical manifestations in different gender. Methods: High-resolution T1-weighted images were acquired from 61 patients with MDD and 61 healthy controls (36 females and 25 males, both). The sex differences in CT and SA were obtained using the FreeSurfer software and compared between every two groups by post hoc test. Spearman correlation analysis was also performed to explore the relationships between these regions and clinical characteristics. Results: In male patients with MDD, the CT of the right precentral was thinner compared to female patients, although this did not survive Bonferroni correction. The SA of several regions, including right superior frontal, medial orbitofrontal gyrus, inferior frontal gyrus triangle, superior temporal, middle temporal, lateral occipital gyrus, and inferior parietal lobule in female patients with MDD was smaller than that in male patients (P < 0.01 after Bonferroni correction). In female patients, the SA of the right superior temporal (r = 0.438, P = 0.008), middle temporal (r = 0.340, P = 0.043), and lateral occipital gyrus (r = 0.372, P = 0.025) were positively correlated with illness duration. Conclusion: The current study provides evidence of sex differences in CT and SA in patients with MDD, which may improve our understanding of the sex-specific neuroanatomical changes in the development of MDD.

SPAMRI: A MATLAB Toolbox for Surface-Based Processing and Analysis of Magnetic Resonance Imaging.

Structural magnetic resonance imaging (MRI) has elicited increasing attention in morphological surface studies due to its stability and sensitivity to neurodegenerative processes, particularly in exploring brain aging and psychiatric disease. However, a user-friendly toolbox for the surface-based analysis of structural MRI is still lacking. On the basis of certain software functions in FreeSurfer, CAT and ANTs, a MATLAB toolbox called "surface-based processing and analysis of MRI" (SPAMRI) has been developed, which can be performed in Windows, Linux and Mac-OS. SPAMRI contains several features as follows: (1) open-source MATLAB-based package with a graphical user interface (GUI); (2) a set of images that can be generated for quality checking, such as Talairach transform, skull strip, and surface reconstruction; (3) user-friendly GUI with capabilities on statistical analysis, multiple comparison corrections, reporting of results, and surface measurement extraction; and (4) provision of a conversion tool between surface files (e.g., mesh files) and volume files (e.g., NIFTI files). SPAMRI is applied to a publicly released structural MRI dataset of 44 healthy young adults and 39 old adults. Findings showed that old people have decreased cortical thickness, especially in prefrontal cortex, relative to those of young adults, thereby suggesting a cognitive decline in the former. SPAMRI is anticipated to substantially simplify surface-based image processing and MRI dataset analyses and subsequently open new opportunities to investigate structural morphologies.

Use of machine learning in predicting the efficacy of repetitive transcranial magnetic stimulation on treating depression based on functional and structural thalamo-prefrontal connectivity: A pilot study.

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive, safe, and efficacious treatment for major depressive disorder (MDD). However, the antidepressant efficacy of rTMS greatly varies across individual patients. Thus, markers that can be used to predict the outcome of rTMS treatment at the individual level must be identified. Thalamo-cortical connectivity was abnormal in patients with MDD, and was normalized after rTMS treatment. In the current study, we investigated whether the resting-state functional and structural thalamo-cortical connectivity could be utilized to predict the rTMS treatment efficacy by employing support vector machine regression analysis. Results showed that the Hamilton Depression Scale scores of patients with MDD decreased after rTMS treatment. The functional connectivity of mediodorsal nucleus with prefrontal cortex predicted the rTMS treatment improvement, whereas the functional connectivity of other thalamic nuclei with cerebral cortex did not predict the treatment efficacy. The brain areas that contributed the most to the prediction were dorsal lateral prefrontal cortex, ventral lateral, and orbital and medial prefrontal areas. The improvement in the outcome of rTMS treatment could also be predicted by the thalamo-prefrontal structural connectivity. No statistically significantly difference in thalamo-cortical connectivity was observed between early improvers and early non-improvers. These results suggested that the thalamo-prefrontal connectivity can predict the rTMS treatment improvement. This study highlighted the crucial role of the thalamo-prefrontal connectivity as a neuroimaging marker in the treatment of depression via rTMS.

Alteration of regional heterogeneity and functional connectivity for obese undergraduates: evidence from resting-state fMRI.

Obesity was found to be related with the changes of brain functions in human beings. There were several brain areas that were verified to be correlated with the obesity, including the parietal cortex, frontal cortex and so on. However, the cortical regions found from different studies were discrepant due to the different ages, gender distribution and satiation degree of participants. We found that the regional homogeneity of right angular gyrus were smaller in obese undergraduates than that in normal-weight undergraduates. Moreover, functional connectivity of the left middle temporal cortex and the right angular gyrus were found to be smaller in obese group than that in normal-weight group by setting the right angular gyrus as seed region. In addition, multiple regression analysis suggested that the right superior frontal gyrus and left middle temporal gyrus were significantly correlated with their body mass index for normal-weight undergraduates, but no significant correlation was found for obese group. In summary, these findings indicated the functional changes of the cortex in obese undergraduates, which might be significant for providing imaging-based biomarkers for intervention and therapy of obesity.