scholarly journals Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hanxiaoran Li ◽  
Sutao Song ◽  
Donglin Wang ◽  
Zhonglin Tan ◽  
Zhenzhen Lian ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Pattern Analysis ◽  
Low Frequency ◽  
Multivariate Pattern Analysis ◽  
Major Depressive ◽  
Individual Level ◽  
Hamd Score ◽  
The Individual ◽  
The Right ◽  
Multivariate Pattern

Abstract Background Magnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA). Methods Seventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus. Results The primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r2 = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r2 = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification. Conclusions The results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

scholarly journals Dynamic Altered Amplitude of Low-Frequency Fluctuations in Patients With Major Depressive Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Ruiping Zheng ◽  
Yuan Chen ◽  
Yu Jiang ◽  
Mengmeng Wen ◽  
Bingqian Zhou ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Neural Activity ◽  
Temporal Variability ◽  
Brain Activity ◽  
Low Frequency ◽  
First Episode ◽  
Posterior Lobe ◽  
Major Depressive ◽  
Drug Naïve ◽  
The Right

Background: Major depressive disorder (MDD) has demonstrated abnormalities of static intrinsic brain activity measured by amplitude of low-frequency fluctuation (ALFF). Recent studies regarding the resting-state functional magnetic resonance imaging (rs-fMRI) have found the brain activity is inherently dynamic over time. Little is known, however, regarding the temporal dynamics of local neural activity in MDD. Here, we investigated whether temporal dynamic changes in spontaneous neural activity are influenced by MDD.Methods: We recruited 81 first-episode, drug-naive MDD patients and 64 age-, gender-, and education-matched healthy controls who underwent rs-fMRI. A sliding-window approach was then adopted for the estimation of dynamic ALFF (dALFF), which was used to measure time-varying brain activity and then compared between the two groups. The relationship between altered dALFF variability and clinical variables in MDD patients was also analyzed.Results: MDD patients showed increased temporal variability (dALFF) mainly focused on the bilateral thalamus, the bilateral superior frontal gyrus, the right middle frontal gyrus, the bilateral cerebellum posterior lobe, and the vermis. Furthermore, increased dALFF variability values in the right thalamus and right cerebellum posterior lobe were positively correlated with MDD symptom severity.Conclusions: The overall results suggest that altered temporal variability in corticocerebellar–thalamic–cortical circuit (CCTCC), involved in emotional, executive, and cognitive, is associated with drug-naive, first-episode MDD patients. Moreover, our study highlights the vital role of abnormal dynamic brain activity in the cerebellar hemisphere associated with CCTCC in MDD patients. These findings may provide novel insights into the pathophysiological mechanisms of MDD.

scholarly journals Multivariate Pattern Analysis of fMRI in Breast Cancer Survivors and Healthy Women

2013 ◽  
Vol 20 (4) ◽  
pp. 391-401 ◽  
Author(s):  
S.M. Hadi Hosseini ◽  
Shelli R. Kesler
Keyword(s):  
Breast Cancer ◽  
Functional Connectivity ◽  
Breast Cancer Survivors ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Survival Rates ◽  
Connectivity Pattern ◽  
Multivariate Pattern Analysis ◽  
The Right ◽  
Multivariate Pattern

AbstractAdvances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C−) BC females and healthy female controls (HC). Twenty-seven C+, 29 C−, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C− groups (71%, p = .012). However, the accuracy of discrimination between C− and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C− groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C−. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. (JINS, 2013, 19, 1–11)

scholarly journals An Application of Univariate and Multivariate Approaches in fMRI to Quantifying the Hemispheric Lateralization of Acoustic and Linguistic Processes

2012 ◽  
Vol 24 (3) ◽  
pp. 636-652 ◽  
Author(s):  
Carolyn McGettigan ◽  
Samuel Evans ◽  
Stuart Rosen ◽  
Zarinah K. Agnew ◽  
Poonam Shah ◽  
...  
Keyword(s):  
Speech Intelligibility ◽  
Pattern Analysis ◽  
Temporal Cortex ◽  
Hemispheric Lateralization ◽  
Multivariate Pattern Analysis ◽  
Hemispheric Asymmetries ◽  
Intelligible Speech ◽  
Hemisphere Dominance ◽  
The Right ◽  
Multivariate Pattern

The question of hemispheric lateralization of neural processes is one that is pertinent to a range of subdisciplines of cognitive neuroscience. Language is often assumed to be left-lateralized in the human brain, but there has been a long running debate about the underlying reasons for this. We addressed this problem with fMRI by identifying the neural responses to amplitude and spectral modulations in speech and how these interact with speech intelligibility to test previous claims for hemispheric asymmetries in acoustic and linguistic processes in speech perception. We used both univariate and multivariate analyses of the data, which enabled us to both identify the networks involved in processing these acoustic and linguistic factors and to test the significance of any apparent hemispheric asymmetries. We demonstrate bilateral activation of superior temporal cortex in response to speech-derived acoustic modulations in the absence of intelligibility. However, in a contrast of amplitude-modulated and spectrally modulated conditions that differed only in their intelligibility (where one was partially intelligible and the other unintelligible), we show a left dominant pattern of activation in STS, inferior frontal cortex, and insula. Crucially, multivariate pattern analysis showed that there were significant differences between the left and the right hemispheres only in the processing of intelligible speech. This result shows that the left hemisphere dominance in linguistic processing does not arise because of low-level, speech-derived acoustic factors and that multivariate pattern analysis provides a method for unbiased testing of hemispheric asymmetries in processing.

scholarly journals Is there any difference in the fractional amplitude of low-frequency fluctuations between first episode major depressive disorder patients and subclinical depressive subjects?

2020 ◽  
Author(s):  
Fang Xie ◽  
Xiuhang Ruan ◽  
Guoqing Zhang ◽  
Yuting Li ◽  
E Li ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Visual Recognition ◽  
Inferior Frontal Gyrus ◽  
Low Frequency ◽  
Fusiform Gyrus ◽  
First Episode ◽  
Major Depressive ◽  
Frequency Fluctuations ◽  
The Right

Abstract Background To explore the differences in the fractional amplitude of low-frequency fluctuations (fALFF) at the whole-brain level between young adults with major depressive disorder (MDD) and those with Subclinical depression (SD). Methods Thirty-nine first-episode MDD patients, 30 SD subjects, and 37 healthy controls (HCs) were recruited. All participants underwent resting-state fMRI (Rs-fMRI) scans on a 3T MR scanner. We used the fALFF to explore spontaneous neuronal activity between groups. Results Significant differences in the fALFF were observed among the three groups. Compared with the HCs, an increased fALFF was found in the left cerebellum in MDD patients. When MDD patients were compared with SD subjects, we observed increased fALFF values in the bilateral fusiform gyrus and decreased fALFF values in the right inferior frontal gyrus, right superior frontal gyrus, right middle frontal gyrus, left cuneus and right precuneus. Compared with the HCs, the SD group demonstrated increased fALFF values in the precuneus. Additionally, a positive correlated was revealed between the fALFF values and Hamilton Anxiety Scale (HAMA)score in the right fusiform gyrus in MDD patients. Moreover, the fALFF value were negatively correlated with the Beck Depression Inventory (BDI) score in the right inferior frontal gyrus and with the age in the left fusiform gyrus in SD subjects. Conclusions Our findings suggest that alterations of cognitive and executive networks, default mode networks and visual recognition circuits may contribute to the different neural mechanisms between MDD and SD in young adult subjects.

scholarly journals Altered Fractional Amplitude of Low-Frequency Fluctuation in Major Depressive Disorder and Bipolar Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Yan Qiu ◽  
Min Yang ◽  
Sujuan Li ◽  
Ziwei Teng ◽  
Kun Jin ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Pearson Correlation ◽  
Low Frequency ◽  
Frequency Fluctuation ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Major Depressive ◽  
The Right

Background: Discriminating between major depressive disorder (MDD) and bipolar disorder (BD) remains challenging and cognitive deficits in MDD and BD are generally recognized. In this study, the fractional amplitude of low-frequency fluctuation (fALFF) approach was performed to explore neural activity and cognition in first-episode, drug-naïve BD and MDD patients, as well as the relationship between altered fALFF values and clinical or psychometric variables.Methods: A total of 21 BD patients, 25 MDD patients, and 41 healthy controls (HCs) completed clinical assessments and resting-state functional magnetic resonance imaging (rs-fMRI) scans in this study. The rs-fMRI data were analyzed by fALFF method and Pearson correlation analyses were performed between altered fALFF values and clinical variables or cognition. Support vector machine (SVM) was adopted to identify the three groups from each other with abnormal fALFF values in the brain regions obtained by group comparisons.Results: (1) The fALFF values were significantly different in the frontal lobe, temporal lobe, and left precuneus among three groups. In comparison to HCs, BD showed increased fALFF values in the right inferior temporal gyrus (ITG) and decreased fALFF values in the right middle temporal gyrus, while MDD showed decreased fALFF values in the right cerebellar lobule IV/V. In comparison to MDD, BD showed decreased fALFF values in bilateral posterior cingulate gyrus and the right cerebellar lobule VIII/IX. (2) In the BD group, a negative correlation was found between increased fALFF values in the right ITG and years of education, and a positive correlation was found between decreased fALFF values in the right cerebellar lobule VIII/IX and visuospatial abilities. (3) The fALFF values in the right cerebellar lobule VIII/IX may have the ability to discriminate BD patients from MDD patients, with sensitivity, specificity, and accuracy all over 0.70.Conclusions: Abnormal brain activities were observed in BD and MDD and were related with cognition in BD patients. The abnormality in the cerebellum can be potentially used to identify BD from MDD patients.

scholarly journals Abnormal Fractional Amplitude of Low-Frequency Fluctuation as a Potential Imaging Biomarker for First-Episode Major Depressive Disorder: A Resting-State fMRI Study and Support Vector Machine Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Yujun Gao ◽  
Xi Wang ◽  
Zhenying Xiong ◽  
Hongwei Ren ◽  
Ruoshi Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Major Depressive Disorder ◽  
Resting State ◽  
Low Frequency ◽  
Diagnostic Value ◽  
First Episode ◽  
Support Vector ◽  
Healthy Controls ◽  
Major Depressive ◽  
The Right

Objective: Major depressive disorder (MDD) is a psychiatric disorder with serious negative health outcomes; however, there is no reliable method of diagnosis. This study explored the clinical diagnostic value of the fractional amplitude of low-frequency fluctuation (fALFF) based on the support vector machine (SVM) method for the diagnosis of MDD.Methods: A total of 198 first-episode MDD patients and 234 healthy controls were involved in this study, and all participants underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Imaging data were analyzed with the fALFF and SVM methods.Results: Compared with the healthy controls, the first-episode MDD patients showed higher fALFF in the left mid cingulum, right precuneus, and left superior frontal gyrus (SFG). The increased fALFF in these three brain regions was positively correlated with the executive control reaction time (ECRT), and the increased fALFF in the left mid cingulum and left SFG was positively correlated with the 17-item Hamilton Rating Scale for Depression (HRSD-17) scores. The SVM results showed that increased fALFF in the left mid cingulum, right precuneus, and left SFG exhibited high diagnostic accuracy of 72.92% (315/432), 71.76% (310/432), and 73.84% (319/432), respectively. The highest diagnostic accuracy of 76.39% (330/432) was demonstrated for the combination of increased fALFF in the right precuneus and left SFG, along with a sensitivity of 84.34% (167/198), and a specificity of 70.51% (165/234).Conclusion: Increased fALFF in the left mid cingulum, right precuneus, and left SFG may serve as a neuroimaging marker for first-episode MDD. The use of the increased fALFF in the right precuneus and left SFG in combination showed the best diagnostic value.

Sign in / Sign up

Export Citation Format

Share Document