scholarly journals Cortical thickness distinguishes between major depression and schizophrenia in adolescents

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zheyi Zhou ◽  
Kangcheng Wang ◽  
Jinxiang Tang ◽  
Dongtao Wei ◽  
Li Song ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Cortical Thickness ◽  
Psychotic Disorders ◽  
Support Vector ◽  
Healthy Controls ◽  
Classification Feature ◽  
Highest Weight ◽  
Temporal Pole ◽  
The Right ◽  
Leave One Out

Abstract Background Early diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents. Methods We obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls. Results We found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight. Conclusions Our findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.

scholarly journals Brain structural correlates of schizotypal signs and subclinical schizophrenia nuclear symptoms in healthy individuals

2020 ◽  
pp. 1-10
Author(s):  
Tina Meller ◽  
Simon Schmitt ◽  
Ulrich Ettinger ◽  
Phillip Grant ◽  
Frederike Stein ◽  
...  
Keyword(s):  
Negative Correlation ◽  
Psychotic Disorders ◽  
Structural Parameters ◽  
Brain Structures ◽  
Precentral Gyrus ◽  
Healthy Individuals ◽  
Positive Correlation ◽  
Temporal Pole ◽  
The Right ◽  
Cluster Level

Abstract Background Subclinical psychotic-like experiences (PLE), resembling key symptoms of psychotic disorders, are common throughout the general population and possibly associated with psychosis risk. There is evidence that such symptoms are also associated with structural brain changes. Methods In 672 healthy individuals, we assessed PLE and associated distress with the symptom-checklist-90R (SCL-90R) scales ‘schizotypal signs’ (STS) and ‘schizophrenia nuclear symptoms’ (SNS) and analysed associations with voxel- and surfaced-based brain structural parameters derived from structural magnetic resonance imaging at 3 T with CAT12. Results For SNS, we found a positive correlation with the volume in the left superior parietal lobule and the precuneus, and a negative correlation with the volume in the right inferior temporal gyrus [p < 0.05 cluster-level Family Wise Error (FWE-corrected]. For STS, we found a negative correlation with the volume of the left and right precentral gyrus (p < 0.05 cluster-level FWE-corrected). Surface-based analyses did not detect any significant clusters with the chosen statistical threshold of p < 0.05. However, in exploratory analyses (p < 0.001, uncorrected), we found a positive correlation of SNS with gyrification in the left insula and rostral middle frontal gyrus and of STS with the left precuneus and insula, as well as a negative correlation of STS with gyrification in the left temporal pole. Conclusions Our results show that brain structures in areas implicated in schizophrenia are also related to PLE and its associated distress in healthy individuals. This pattern supports a dimensional model of the neural correlates of symptoms of the psychotic spectrum.

scholarly journals Abnormal Default-Mode Network Homogeneity in Melancholic and Nonmelancholic Major Depressive Disorder at Rest

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Meiqi Yan ◽  
Xilong Cui ◽  
Feng Liu ◽  
Huabing Li ◽  
Renzhi Huang ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Healthy Controls ◽  
Major Depressive ◽  
Middle Temporal ◽  
Inferior Temporal Gyrus ◽  
Medial Frontal Gyrus ◽  
The Right

Background. Melancholic depression has been assumed as a severe type of major depressive disorder (MDD). We aimed to explore if there were some distinctive alterations in melancholic MDD and whether the alterations could be used to discriminate the melancholic MDD and nonmelancholic MDD. Methods. Thirty-one outpatients with melancholic MDD, thirty-three outpatients with nonmelancholic MDD, and thirty-two age- and gender-matched healthy controls were recruited. All participants were scanned by resting-state functional magnetic resonance imaging (fMRI). Imaging data were analyzed with the network homogeneity (NH) and support vector machine (SVM) methods. Results. Both patient groups exhibited increased NH in the right PCC/precuneus and right angular gyrus and decreased NH in the right middle temporal gyrus compared with healthy controls. Compared with nonmelancholic patients and healthy controls, melancholic patients exhibited significantly increased NH in the bilateral superior medial frontal gyrus and decreased NH in the left inferior temporal gyrus. But merely for melancholic patients, the NH of the right middle temporal gyrus was negatively correlated with TEPS total and contextual anticipatory scores. SVM analysis showed that a combination of NH values in the left superior medial frontal gyrus and left inferior temporal gyrus could distinguish melancholic patients from nonmelancholic patients with accuracy, sensitivity, and specificity of 79.66% (47/59), 70.97% (22/31), and 89.29%(25/28), respectively. Conclusion. Our findings showed distinctive network homogeneity alterations in melancholic MDD which may be potential imaging markers to distinguish melancholic MDD and nonmelancholic MDD.

scholarly journals A Real-Time Portable IoT System for Telework Tracking

2021 ◽  
Vol 3 ◽  
Author(s):  
Yongxin Zhang ◽  
Zheng Chen ◽  
Haoyu Tian ◽  
Koshiro Kido ◽  
Naoaki Ono ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Tracking System ◽  
Well Being ◽  
Support Vector ◽  
Working At Home ◽  
Accurate Performance ◽  
Working Status ◽  
The Right ◽  
Leave One Out

Telework has become a universal working style under the background of COVID-19. With the increased time of working at home, problems, such as lack of physical activities and prolonged sedentary behavior become more prominent. In this situation, a self-managing working pattern regulation may be the most practical way to maintain worker's well-being. To this end, this paper validated the idea of using an Internet of Things (IoT) system (a smartphone and the accompanying smartwatch) to monitor the working status in real-time so as to record the working pattern and nudge the user to have a behavior change. By using the accelerometer and gyroscope enclosed in the smartwatch worn on the right wrist, nine-channel data streams of the two sensors were sent to the paired smartphone for data preprocessing, and action recognition in real time. By considering the cooperativity and orthogonality of the data streams, a shallow convolutional neural network (CNN) model was constructed to recognize the working status from a common working routine. As preliminary research, the results of the CNN model show accurate performance [5-fold cross-validation: 0.97 recall and 0.98 precision; leave-one-out validation: 0.95 recall and 0.94 precision; (support vector machine (SVM): 0.89 recall and 0.90 precision; random forest: 0.95 recall and 0.93 precision)] for the recognition of working status, suggesting the feasibility of this fully online method. Although further validation in a more realistic working scenario should be conducted for this method, this proof-of-concept study clarifies the prospect of a user-friendly online working tracking system. With a tailored working pattern guidance, this method is expected to contribute to the workers' wellness not only during the COVID-19 pandemic but also take effect in the post-COVID-19 era.

scholarly journals Neural substrate of quality of life in patients with schizophrenia: a magnetisation transfer imaging study

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Catherine Faget-Agius ◽  
Laurent Boyer ◽  
Jonathan Wirsich ◽  
Jean-Philippe Ranjeva ◽  
Raphaelle Richieri ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Imaging Study ◽  
Magnetization Transfer Ratio ◽  
Healthy Controls ◽  
Magnetisation Transfer ◽  
Magnetisation Transfer Imaging ◽  
Temporal Pole ◽  
Neural Substrate ◽  
The Right

Abstract The aim of this study was to investigate the neural substrate underlying quality of life (QoL) and to demonstrate the microstructural abnormalities associated with impaired QoL in a large sample of patients with schizophrenia, using magnetisation transfer imaging. A total of 81 right-handed men with a diagnosis of schizophrenia and 25 age- and sex-similar healthy controls were included and underwent a 3T MRI with magnetization transfer ratio (MTR) to detect microstructural abnormalities. Compared with healthy controls, patients with schizophrenia had grey matter (GM) decreased MTR values in the temporal lobe (BA21, BA37 and BA38), the bilateral insula, the occipital lobe (BA17, BA18 and BA19) and the cerebellum. Patients with impaired QoL had lower GM MTR values relative to patients with preserved QoL in the bilateral temporal pole (BA38), the bilateral insula, the secondary visual cortex (BA18), the vermis and the cerebellum. Significant correlations between MTR values and QoL scores (p < 0.005) were observed in the GM of patients in the right temporal pole (BA38), the bilateral insula, the vermis and the right cerebellum. Our study shows that QoL impairment in patients with schizophrenia is related to the microstructural changes in an extensive network, suggesting that QoL is a bio-psychosocial marker.

Interictal photosensitivity associates with altered brain structure in patients with episodic migraine

Cephalalgia ◽  
2015 ◽  
Vol 36 (6) ◽  
pp. 526-533 ◽  
Author(s):  
Catherine D Chong ◽  
Amaal J Starling ◽  
Todd J Schwedt
Keyword(s):  
Cortical Thickness ◽  
Brain Structure ◽  
Functional Neuroimaging ◽  
Light Sensitivity ◽  
Brain Regions ◽  
Episodic Migraine ◽  
Healthy Controls ◽  
The Right ◽  
Episodic Migraineurs ◽  
Assessment Questionnaire

Background Migraine attacks manifest with hypersensitivities to light, sound, touch and odor. Some people with migraine have photosensitivity between migraine attacks, suggesting persistent alterations in the integrity of brain regions that process light. Although functional neuroimaging studies have shown visual stimulus induced “hyperactivation” of visual cortex regions in migraineurs between attacks, whether photosensitivity is associated with alterations in brain structure is unknown. Methods Levels of photosensitivity were evaluated using the Photosensitivity Assessment Questionnaire in 48 interictal migraineurs and 48 healthy controls. Vertex-by-vertex measurements of cortical thickness were assessed in 28 people with episodic migraine who had interictal photosensitivity (mean age = 35.0 years, SD = 12.1) and 20 episodic migraine patients without symptoms of interictal photosensitivity (mean age = 36.0 years, SD = 11.4) using a general linear model design. Results Migraineurs have greater levels of interictal photosensitivity relative to healthy controls. Relative to migraineurs without interictal photosensitivity, migraineurs with interictal photosensitivity have thicker cortex in several brain areas including the right lingual, isthmus cingulate and pericalcarine regions, and the left precentral, postcentral and supramarginal regions. Conclusion Episodic migraineurs with interictal photosensitivity have greater cortical thickness in the right parietal-occipital and left fronto-parietal regions, suggesting that persistent light sensitivity is associated with underlying structural alterations.

scholarly journals Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

2021 ◽  
Author(s):  
Helena Pelin ◽  
Marcus Ising ◽  
Frederike Stein ◽  
Susanne Meinert ◽  
Tina Meller ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Psychotic Disorders ◽  
Characteristic Curve ◽  
Well Being ◽  
Healthy Controls ◽  
Discovery Sample ◽  
Polygenic Scores ◽  
Genetic Features ◽  
Molecular Etiology ◽  
Disease Stages

AbstractPsychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1–3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.

scholarly journals Surface-based Map Plasticity of the Sensorimotor Cortex in Hip Disorder at Local and Extensive Levels: A Resting-state fMRI Study

2020 ◽  
Author(s):  
Jie Ma ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Jia-Jia Wu ◽  
Bei-Bei Huo ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Cortical Thickness ◽  
Resting State ◽  
Primary Motor Cortex ◽  
Premotor Cortex ◽  
Frontal Eye Field ◽  
Healthy Controls ◽  
Eye Field ◽  
Somatotopic Map ◽  
The Right

Abstract Background: Pain is one of the manifestations of hip disorder and has been proven to lead to the remodeling of somatotopic map plasticity in the cortex. However, it’s not clear whether hip disorder with pain induces somatotopic map plasticity in the cortex. We aimed to evaluate the surface-based map plasticity of the somatotopic cortex in hip disorder at local and extensive levels by resting-state functional magnetic resonance imaging (rs-fMRI).Methods: 20 patients with osteonecrosis of the femoral head (ONFH) (12 males and 8 females, age= 56.80±13.60 years) with Visual Analogue Scale (VAS) scores ≥ 4 and 20 healthy controls (9 males and 11 females, age= 54.56±10.23 years) were enrolled in this study. rs-fMRI data and T1 imaging data were collected, and surface-based regional homogeneity (ReHo), seed-based functional connectivity (FC), cortical thickness and the volume of subcortical gray nuclei were calculated.Results: Compared with the healthy controls, the ONFH patients showed significantly increased surface-based ReHo in areas distributed mainly in the left dorsolateral prefrontal cortex and frontal eye field, the right frontal eye field and the premotor cortex and decreased surface-based ReHo in the right primary motor cortex and primary sensory cortex. When the area with decreased surface-based ReHo in the frontal eye field and right premotor cortex was used as the regions of interest (ROI), compared with the controls, the ONFH patients displayed increased FC in the right middle frontal cortex and right inferior parietal cortex and decreased FC in the right precentral cortex and right middle occipital cortex. ONFH patients also showed significantly decreased cortical thickness in the para-insular area, supplementary motor cortex area and frontal eye field and decreased volume of subcortical gray matter nuclei in the right nucleus accumbens (479.32±88.26 vs 539.44±68.36, P=0.026). Conclusions: Hip disorder patients showed cortical plasticity changes, mainly in sensorimotor and pain-related regions.

scholarly journals Widespread Cortical Thickness Reductions Following Non-medical Use of Ketamine: a Structural MRI Study of Individuals with Ketamine Dependence

2021 ◽  
Author(s):  
Jinsong Tang ◽  
Qiuxia Wu ◽  
Chang Qi ◽  
An Xie ◽  
Jianbin Liu ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Structural Mri ◽  
Brain Regions ◽  
Analysis Of Covariance ◽  
P Value ◽  
Healthy Controls ◽  
Brain Images ◽  
Dorsolateral Prefrontal ◽  
The Right ◽  
Mri Study

AbstractBackgroundA version of ketamine, called Esketamine has been approved for treatment-resistant depression (TRD). Ketamine (“K powder”), a “dissociative” anesthetic agent, however, has been used non-medically alone or with other illicit substances. Our previous studies showed a link between non-medical ketamine use and brain structural and functional alterations. We found dorsal prefrontal gray matter reduction in chronic ketamine users. It is unknown, however, whether these observations might parallel findings of cortical thickness alterations. This study aimed at exploring cortical thickness abnormalities following non-medical, long-term use of ketamine.MethodsStructural brain images were acquired for 95 patients with ketamine dependence, and 169 drug-free healthy controls. FreeSurfer software was used to measure cortical thickness for 68 brain regions. Cortical thickness was compared between the two groups using analysis of covariance (ANCOVA) with covariates of age, gender, educational level, smoking, drinking, and whole brain mean cortical thickness. Results were considered significant if the Bonferroni corrected P-value < 0.01.ResultsCompared to healthy controls, patients with ketamine dependence have widespread decreased cortical thickness, with the most extensive reductions in the frontal (including the dorsolateral prefrontal cortex, DLPFC) and parietal (including the precuneus) lobes. Increased cortical thickness was not observed in ketamine users relative to comparison subjects. Estimated total lifetime ketamine consumption is correlated with the right inferior parietal and the right rostral middle frontal cortical thickness reductions.ConclusionsThis study provides first evidence that, compared with healthy controls, chronic ketamine users had cortical thickness reductions.

scholarly journals Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

2021 ◽  
Author(s):  
Helena Pelin ◽  
Marcus Ising ◽  
Frederike Stein ◽  
Susanne Meinert ◽  
Tina Meller ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Psychotic Disorders ◽  
Clinical Manifestations ◽  
Well Being ◽  
Classification Systems ◽  
Therapeutic Interventions ◽  
Healthy Controls ◽  
Discovery Sample ◽  
Polygenic Scores ◽  
Genetic Features

AbstractPsychiatric disorders show heterogeneous clinical manifestations and disease trajectories, with current classification systems not accurately reflecting their molecular etiology. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify diagnostically mixed psychiatric patient clusters that share clinical and genetic features and may profit from similar therapeutic interventions. We used unsupervised high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N=1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, was characterized by general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. MDD patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N=622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction AUC=81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatment regimes.

Structural correlates of trait impulsivity in patients with bipolar disorder and healthy controls: a surface-based morphometry study

2017 ◽  
Vol 47 (7) ◽  
pp. 1292-1299 ◽  
Author(s):  
P. C. Tu ◽  
Y. H. Kuan ◽  
C. T. Li ◽  
T. P. Su
Keyword(s):  
Bipolar Disorder ◽  
Cortical Thickness ◽  
Mood State ◽  
Healthy Controls ◽  
Resonance Imaging ◽  
Structural Magnetic Resonance Imaging ◽  
Clinical Evaluations ◽  
Trait Impulsivity ◽  
The Right ◽  
Surface Based Morphometry

BackgroundPatients with bipolar disorder (BD) frequently exhibit impulsive behaviors independent of their mood state, and trait impulsivity is increasingly recognized as a crucial BD biomarker. This study aimed to investigate structural correlates of trait impulsivity measured using the Barratt Impulsiveness Scale (BIS) in healthy controls (HCs) and patients with BD.MethodWe recruited 59 patients diagnosed with BD I or BD II (35.3 ± 8.5 years) and 56 age- and sex-matched HCs (33.9 ± 7.4 years). Participants underwent structural magnetic resonance imaging and clinical evaluations, and their BIS scores were evaluated. An automated surface-based method (FreeSurfer) was used to measure cortical thickness and generate thickness maps for each participant. Brain-wise regression analysis of the association between cortical thickness and BIS scores was performed separately for BD and HC groups by using a general linear model.ResultsPatients with BD obtained significantly higher BIS scores than HCs. In HCs, higher BIS scores were associated with a thinner cortex in the left inferior, middle and medial frontal cortices. By contrast, in BD patients, higher BIS scores were associated with a thicker cortex in the right insula. Patients with BD showed a thinner cortex than HCs in all these four structures.ConclusionsThe findings indicate that the left prefrontal cortex plays a cardinal role in trait impulsivity of healthy individuals. Patients with BD have a different structural correlate of trait impulsivity in the right insula. However, the use of various psychotropics in patients with BD may limit our interpretation of BD findings.

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