scholarly journals Resting-state functional connectivity and psychopathology in Klinefelter syndrome (47, XXY)

2020 ◽  
Author(s):  
Ethan T. Whitman ◽  
Siyuan Liu ◽  
Erin Torres ◽  
Allysa Warling ◽  
Kathleen Wilson ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Genetic Risk ◽  
Klinefelter Syndrome ◽  
Brain Connectivity ◽  
Brain Anatomy ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Dorsolateral Prefrontal ◽  
Neuroimaging Study

Klinefelter syndrome (47, XXY; Henceforth: XXY syndrome) is a high impact but poorly understood genetic risk factor for neuropsychiatric impairment. Here, we provide the first neuroimaging study to map resting-state functional connectivity (rsFC) changes in XXY syndrome and ask how these might relate to brain anatomy and psychopathology. We collected resting state functional magnetic resonance imaging data from 75 individuals with XXY and 84 healthy XY males. We implemented a brain-wide screen to identify regions with altered global rsFC in XXY vs. XY males, and then used seed-based analysis to decompose these alterations. We further compared rsFC changes with regional changes in brain volume from voxel-based morphometry and tested for correlations between rsFC and symptom variation within XXY syndrome. We found that XXY syndrome was characterized by increased global rsFC in the left dorsolateral prefrontal cortex (DLPFC), associated with overconnectivity with diverse rsFC networks. Regional rsFC changes were partly coupled to regional volumetric changes in XXY syndrome. Within the precuneus, variation in DLPFC rsFC within XXY syndrome was correlated with the severity of psychopathology in XXY individuals. Our findings provide the first view of altered functional brain connectivity in XXY syndrome and delineate links between these alterations and those relating to both brain anatomy and psychopathology. Taken together, these insights advance biological understanding of XXY syndrome as a disorder in its own right, and as a model of genetic risk for psychopathology more broadly.

scholarly journals Mechanisms of transcranial magnetic stimulation in the treatment of anorexia nervosa

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S49-S50
Author(s):  
Lydia Shackshaft
Keyword(s):  
Anorexia Nervosa ◽  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
High Frequency ◽  
Magnetic Stimulation ◽  
Dorsolateral Prefrontal Cortex ◽  
Resting State Functional Connectivity ◽  
Dorsolateral Prefrontal ◽  
Neurophysiological Mechanisms

AimsSevere and Enduring Anorexia Nervosa (SE-AN) is a challenging condition to treat, with limited therapeutic options, high morbidity, and the highest mortality rates of any psychiatric illness. Repetitive Transcranial Magnetic Stimulation (rTMS) is an emerging treatment option, as evidence demonstrates promising efficacy in improving mood and reducing core Anorexia Nervosa symptoms, as well as safety and tolerability to patients. We aimed to investigate the neurophysiological mechanisms of rTMS use in SE-AN patients by assessing changes in resting state functional connectivity, in the first functional neuroimaging analysis investigating rTMS effects in Anorexia Nervosa patients.Method26 females with a current diagnosis of SE-AN received 20 sessions of sham or real high frequency rTMS (10 hertz) to the left dorsolateral prefrontal cortex in a randomised double-blind trial. Resting-state functional magnetic resonance imaging was performed before and after rTMS. Neural correlates of rTMS treatment were identified using a seed-based functional connectivity analysis with the left dorsolateral prefrontal cortex and bilateral amygdalae as regions of interest. Functional connectivity differences were analysed using t-contrasts in a mixed ANOVA (flexible factorial analysis) to assess interactions between treatment group (real rTMS vs sham) and time-point (pre or post TMS).ResultNo statistically significant changes in resting-state functional connectivity were observed post-rTMS compared to baseline in participants receiving active rTMS compared to sham. Increased functional connectivity between the left amygdala and left pre-supplementary motor area was observed to reach cluster-wise significance (PFWE < 0.05). However, after Bonferroni correction for multiple comparisons (3 seed regions), this did not reach the significance threshold PFWE <0.017.ConclusionThis study highlights the need for further investigation of neurophysiological mechanisms, including resting-state functional connectivity modulation, resulting from rTMS to the dorsolateral prefrontal cortex in SE-AN patients. This requires higher powered studies to account for heterogeneity in treatment response. We have provided some indication that high frequency rTMS may have therapeutic benefit in SE-AN by modification of functional connectivity between prefrontal and limbic brain regions, resulting in improved top-down cognitive control over emotional processing and ability to enact goal-directed behaviours, enabling secondary reductions in eating disorder behaviours.

scholarly journals Disturbed Resting-State Whole-Brain Functional Connectivity of Striatal Subregions in Bulimia Nervosa

2020 ◽  
Vol 23 (6) ◽  
pp. 356-365
Author(s):  
Li Wang ◽  
Kun Bi ◽  
Zhou Song ◽  
Zhe Zhang ◽  
Ke Li ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Bulimia Nervosa ◽  
Sensorimotor Cortex ◽  
Resting State ◽  
Parietal Lobe ◽  
Self Regulation ◽  
Imaging Data ◽  
Healthy Women ◽  
Dorsolateral Prefrontal ◽  
Primary Sensorimotor Cortex

Abstract Background Disturbed self-regulation, taste reward, as well as somatosensory and visuospatial processes were thought to drive binge eating and purging behaviors that characterize bulimia nervosa. Although studies have implicated a central role of the striatum in these dysfunctions, there have been no direct investigations on striatal functional connectivity in bulimia nervosa from a network perspective. Methods We calculated the functional connectivity of striatal subregions based on the resting-state functional Magnetic Resonance Imaging data of 51 bulimia nervosa patients and 53 healthy women. Results Compared with the healthy women, bulimia nervosa patients showed increased positive functional connectivity in bilateral striatal nuclei and thalamus for nearly all of the striatal subregions, and increased negative functional connectivity in bilateral primary sensorimotor cortex and occipital areas for both ventral striatum and putamen subregions. Only for the putamen subregions, we observed reduced negative functional connectivity in the prefrontal (bilateral superior and middle frontal gyri) and parietal (right inferior parietal lobe and precuneus) areas. Several striatal connectivities with occipital and primary sensorimotor cortex significantly correlated with the severity of bulimia. Conclusions The findings indicate bulimia nervosa-related alterations in striatal functional connectivity with the dorsolateral prefrontal cortex supporting self-regulation, the subcortical striatum and thalamus involved in taste reward, as well as the visual occipital and sensorimotor regions mediating body image, which contribute to our understanding of neural circuitry of bulimia nervosa and encourage future therapeutic developments for bulimia nervosa by modulating striatal pathway.

scholarly journals Sex-dependent and sex-independent brain resting-state functional connectivity in children with autism spectrum disorder

2016 ◽  
Author(s):  
Xin Di ◽  
Bharat B Biswal
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Main Effects

Background: Males are more likely to suffer from autism spectrum disorder (ASD) than females. As to whether females with ASD have similar brain alterations remain an open question. The current study aimed to examine sex-dependent as well as sex-independent alterations in resting-state functional connectivity in individuals with ASD compared with typically developing (TD) individuals. Method: Resting-state functional MRI data were acquired from the Autism Brain Imaging Data Exchange (ABIDE). Subjects between 6 to 20 years of age were included for analysis. After matching the intelligence quotient between groups for each dataset, and removing subjects due to excessive head motion, the resulting effective sample contained 28 females with ASD, 49 TD females, 129 males with ASD, and 141 TD males, with a two (diagnosis) by two (sex) design. Functional connectivity among 153 regions of interest (ROIs) comprising the whole brain was computed. Two by two analysis of variance was used to identify connectivity that showed diagnosis by sex interaction or main effects of diagnosis. Results: The main effects of diagnosis were found mainly between visual cortex and other brain regions, indicating sex-independent connectivity alterations. We also observed two connections whose connectivity showed diagnosis by sex interaction between the precuneus and medial cerebellum as well as the precunes and dorsal frontal cortex. While males with ASD showed higher connectivity in these connections compared with TD males, females with ASD had lower connectivity than their counterparts. Conclusions: Both sex-dependent and sex-independent functional connectivity alterations are present in ASD.

scholarly journals Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling

2021 ◽  
Vol 15 ◽  
Author(s):  
Naoki Okamoto ◽  
Hiroyuki Akama
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Cross Validation ◽  
Data Augmentation ◽  
Rapid Change ◽  
Autism Spectrum ◽  
Batch Size ◽  
Imaging Data ◽  
Resting State Functional Connectivity

Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; however, unlike the original IIC, it is characterized by transfer learning with labeled data pairs, but without the need for a data augmentation technique. Each site in LOSO-CV is left out in turn from the remaining sites used for training and receives a value for modeling evaluation. We applied the EIIC to the resting state functional connectivity magnetic resonance imaging dataset of the Autism Brain Imaging Data Exchange. The challenging nature of brain analysis for autism spectrum disorder (ASD) can be attributed to the variability of subjects, particularly the rapid change in the neural system of children as the target ASD age group. However, EIIC demonstrated higher LOSO-CV classification accuracy for the majority of scanning locations than previously used methods. Particularly, with the adjustment of a mini-batch size, EIIC outperformed other classifiers with an accuracy &gt;0.8 for the sites with highest mean age of the subjects. Considering its effectiveness, our proposed method might be promising for harmonization in other domains, owing to its simplicity and intrinsic flexibility.

scholarly journals Brain Connectivity Tracks Effects of Chemotherapy Separately from Behavioral Measures

2018 ◽  
Author(s):  
Omid Kardan ◽  
Mary K. Askren ◽  
Misook Jung ◽  
Scott Peltier ◽  
Bratislav Misic ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Performance ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Self Report ◽  
Breast Cancer Patients ◽  
Resting State Functional Connectivity ◽  
Behavioral Measures ◽  
Cognitive Health

AbstractSeveral studies in cancer research have suggested that cognitive dysfunction following chemotherapy, referred to in lay terms as “chemobrain”, is a serious problem. At present, the changes in integrative brain function that underlie such dysfunction remains poorly understood. Recent developments in neuroimaging suggest that patterns of functional connectivity can provide a broadly applicable neuromarker of cognitive performance and other psychometric measures. The current study used multivariate analysis methods to identify patterns of disruption in resting state functional connectivity of the brain due to chemotherapy and the degree to which the disruptions can be linked to behavioral measures of distress and cognitive performance. Sixty two women (22 healthy control, 18 patients treated with adjuvant chemotherapy, and 22 treated without chemotherapy) were evaluated with neurocognitive measures followed by self-report questionnaires and open eyes resting-state fMRI scanning at three time points: diagnosis (M0, pre-adjuvant treatment), at least 1 month (M1), and 7 months (M7) after treatment. The results indicated deficits in cognitive health of breast cancer patients immediately after chemotherapy that improved over time. This psychological trajectory was paralleled by a disruption and later recovery of resting-state functional connectivity, mostly in the parietal and frontal brain regions. The functional connectivity alteration pattern seems to be a separable treatment symptom from the decreased cognitive health. More targeted support for patients should be developed to ameliorate these multi-faceted side effects of chemotherapy treatment on neural functioning and cognitive health.

scholarly journals Extended Invariant Information Clustering is Effective for Leave-One- Site-Out Cross-Validation in Resting State Functional Connectivity Modelling

2021 ◽  
Author(s):  
Naoki Okamoto ◽  
Hiroyuki Akama
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Cross Validation ◽  
Data Augmentation ◽  
Rapid Change ◽  
Autism Spectrum ◽  
Batch Size ◽  
Imaging Data ◽  
Resting State Functional Connectivity

Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; however, unlike the original IIC, it is characterized by supervised learning with labeled data pairs, but without the need for a data augmentation technique. Each site in LOSO-CV is left out in turn from the remaining sites used for training and receives a value for modeling evaluation. We applied the EIIC to the resting state functional connectivity magnetic resonance imaging dataset of the Autism Brain Imaging Data Exchange. The challenging nature of brain analysis for autism spectrum disorder (ASD) can be attributed to the variability of subjects, particularly the rapid change in the neural system of children as the target ASD age group. However, EIIC demonstrated higher LOSO-CV classification accuracy for the majority of scanning locations than previously used methods. Particularly, with the adjustment of a mini-batch size, EIIC outperformed other classifiers with an accuracy >0.8 for the sites with highest mean age of the subjects. Considering its effectiveness, our proposed method might be promising for harmonization in other domains, owing to its simplicity and intrinsic flexibility. We are currently submitting this manuscript to Frontiers in Neuroinformatics.

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