Connectome-Based Biomarkers Predict Subclinical Depression and Identify Abnormal Brain Connections With the Lateral Habenula and Thalamus

Neurobehavioral Impairments Predict Specific Cerebral Damage in Rat Model of Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is a severe form of stroke that can cause unpredictable and diffuse cerebral damage, which is difficult to detect until it becomes irreversible. Therefore, there is a need for a reliable method to identify dysfunctional regions and initiate treatment before permanent damage occurs. Neurobehavioral assessments have been suggested as a possible tool to detect and approximately localize dysfunctional cerebral regions. In this study, we hypothesized that a neurobehavioral assessment battery could be a sensitive and specific method for detecting damage in discrete cerebral regions following SAH. To test this hypothesis, a behavioral battery was employed at multiple time points after SAH induced via an endovascular perforation, and brain damage was confirmed via postmortem histopathological analysis. Our results demonstrate that impairment of sensorimotor function accurately predict damage in the cerebral cortex (AUC 0.905; sensitivity 81.8%; specificity 90.9%) and striatum (AUC 0.913; sensitivity 90.1%; specificity 100%), while impaired novel object recognition is a more accurate indicator of damage to the hippocampus (AUC 0.902; sensitivity 74.1%; specificity 83.3%) than impaired reference memory (AUC 0.746; sensitivity 72.2%; specificity 58.0%). Tests for anxiety-like and depression-like behaviors predict damage to the amygdala (AUC 0.900; sensitivity 77.0%; specificity 81.7%) and thalamus (AUC 0.963; sensitivity 86.3%; specificity 87.8%), respectively. This study suggests that recurring behavioral testing can accurately predict damage in specific brain regions, which could be developed into a clinical battery for early detection of SAH damage in humans, potentially improving early treatment and outcomes.

Structural and functional brain alterations in subthreshold depression: A multimodal coordinate-based meta-analysis

Imaging studies of subthreshold depression (StD) have reported structural and functional abnormalities in a variety of spatially diverse brain regions. However, there is no consensus among different studies. In the present study, we applied a multimodal meta-analytic approach, the Activation Likelihood Estimation (ALE), to test the hypothesis that StD exhibits spatially convergent structural and functional brain abnormalities compared to healthy controls. A total of 31 articles with 25 experiments were included, collectively representing 1001 subjects with StD. We found consistent differences between StD and healthy controls mainly in the left insula across studies with various neuroimaging methods. Further exploratory analyses found structural atrophy and decreased functional activities in the right pallidum and thalamus in StD, and abnormal spontaneous activity converged to the middle frontal gyrus. Coordinate-based meta-analysis found spatially convergent structural and functional impairments in StD. These findings provide novel insights for understanding the neural underpinnings of subthreshold depression and enlighten the potential targets for its early screening and therapeutic interventions in the future.

Dysregulation of Kappa Opioid Receptor Neuromodulation of Lateral Habenula Synaptic Function following a Repetitive Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) increases the risk of cognitive deficits, affective disorders, anxiety and substance use disorder in affected individuals. Substantial evidence suggests a critical role for the lateral habenula (LHb) in pathophysiology of psychiatric disorders. Recently, we demonstrated a causal link between persistent mTBI-induced LHb hyperactivity due to synaptic excitation/inhibition (E/I) imbalance and motivational deficits in self-care grooming behavior in young adult male mice using a repetitive closed head injury mTBI model. One of the major neuromodulatory systems that is responsive to traumatic brain and spinal cord injuries, influences affective states and also modulates LHb activity is the dynorphin/kappa opioid receptor (Dyn/KOR) system. However, the effects of mTBI on KOR neuromodulation of LHb function is unknown. To address this, we first used retrograde tracing to anatomically verify that the mouse LHb indeed receives Dyn/KOR expressing projections. We identified several major KOR-expressing and Dyn-expressing synaptic inputs projecting to the mouse LHb. We then functionally evaluated the effects of in vitro KOR modulation of spontaneous synaptic activity within the LHb of male and female sham and mTBI mice at 4week post-injury using the repetitive closed head injury mTBI model. Similar to what we previously reported in the LHb of male mTBI mice, mTBI presynaptically diminished spontaneous synaptic activity onto LHb neurons, while shifting synaptic E/I toward excitation in female mouse LHb. Furthermore, KOR activation in either mouse male/female LHb generally suppressed spontaneous glutamatergic transmission without altering GABAergic transmission, resulting in a significant reduction in E/I ratios and decreased excitatory synaptic drive to LHb neurons of male and female sham mice. Interestingly following mTBI, while responses to KOR activation at LHb glutamatergic synapses were observed comparable to those of sham, LHb GABAergic synapses acquired an additional sensitivity to KOR-mediated inhibition. Thus, in contrast to sham LHb, we observed a reduction in GABA release probability in response to KOR stimulation in mTBI LHb, resulting in a chronic loss of KOR-mediated net synaptic inhibition within the LHb. Overall, our findings uncovered the previously unknown sources of major Dyn/KOR-expressing synaptic inputs projecting to the mouse LHb. Further, we demonstrate that an engagement of intra-LHb Dyn/KOR signaling provides a global suppression of excitatory synaptic drive to the mouse LHb which could act as an inhibitory braking mechanism to prevent LHb hyperexcitability. The additional engagement of KOR-mediated modulatory action on LHb GABAergic transmission by mTBI could contribute to the E/I imbalance after mTBI, with Dyn/KOR signaling serving as a disinhibitory mechanism for LHb neurons in male and female mTBI mice.

AKAP150-anchored PKA regulates synaptic transmission and plasticity, neuronal excitability and CRF neuromodulation in the mouse lateral habenula

The scaffolding A-kinase anchoring protein 150 (AKAP150) is critically involved in kinase and phosphatase regulation of synaptic transmission/plasticity, and neuronal excitability. Emerging evidence also suggests that AKAP150 signaling may play a key role in brain's processing of rewarding/aversive experiences, however its role in the lateral habenula (LHb, as an important brain reward circuitry) is completely unknown. Using whole cell patch clamp recordings in LHb of male wildtype and ΔPKA knockin mice (with deficiency in AKAP-anchoring of PKA), here we show that the genetic disruption of PKA anchoring to AKAP150 significantly reduces AMPA receptor-mediated glutamatergic transmission and prevents the induction of presynaptic endocannabinoid-mediated long-term depression in LHb neurons. Moreover, ΔPKA mutation potentiates GABAA receptor-mediated inhibitory transmission while increasing LHb intrinsic excitability through suppression of medium afterhyperpolarizations. ΔPKA mutation-induced suppression of medium afterhyperpolarizations also blunts the synaptic and neuroexcitatory actions of the stress neuromodulator, corticotropin releasing factor (CRF), in mouse LHb. Altogether, our data suggest that AKAP150 complex signaling plays a critical role in regulation of AMPA and GABAA receptor synaptic strength, glutamatergic plasticity and CRF neuromodulation possibly through AMPA receptor and potassium channel trafficking and endocannabinoid signaling within the LHb.

Joint learning of multi-level dynamic brain networks for autism spectrum disorder diagnosis

Graph convolutional networks (GCNs), with their powerful ability to model non-Euclidean graph data, have shown advantages in learning representations of brain networks. However, considering the complexity, multilayeredness, and spatio-temporal dynamics of brain activities, we have identified two limitations in current GCN-based research on brain networks: 1) Most studies have focused on unidirectional information transmission across brain network levels, neglecting joint learning or bidirectional information exchange among networks. 2) Most of the existing models determine node neighborhoods by thresholding or simply binarizing the brain network, which leads to the loss of edge weight information and weakens the model's sensitivity to important information in the brain network. To address the above issues, we propose a multi-level dynamic brain network joint learning architecture based on GCN for autism spectrum disorder (ASD) diagnosis. Specifically, firstly, constructing different-level dynamic brain networks. Then, utilizing joint learning based on GCN for interactive information exchange among these multi-level brain networks. Finally, designing an edge self-attention mechanism to assign different edge weights to inter-node connections, which allows us to pick out the crucial features for ASD diagnosis. Our proposed method achieves an accuracy of 81.5 %. The results demonstrate that our method enables bidirectional transfer of high-order and low-order information, facilitating information complementarity between different levels of brain networks. Additionally, the use of edge weights enhances the representation capability of ASD-related features.

Habenular functional connections are associated with depression state and modulated by ketamine

BACKGROUND

Depression is a widespread mental health disorder with complex neurobiological underpinnings. The habenula, known as the 'anti-reward center', is thought to play a pivotal role in the pathophysiology of depression. This study aims to elucidate the association between the functional connections of the habenula and depression severity and to explore the modulation of these connections by ketamine.

METHODS

We studied 177 participants from a 7-T resting-state functional magnetic resonance imaging subset of the Human Connectome Project dataset to determine the associations between the functional connections of the habenula and depression. Additionally, we analyzed 60 depressed patients from our ketamine database to conduct a preliminary study on alterations in the functional connections of the habenula after ketamine infusions. We also investigated whether the baseline functional connectivity of the habenula is linked to subsequent improvement in depression.

RESULTS

We found that functional connections between the habenula and the substantia nigra, as well as the ventral tegmental area were negatively correlated with depression scores and elevated after ketamine infusions. Furthermore, the connection between the right habenula and the right substantia nigra was negatively associated with the improvement of depression.

LIMITATIONS

The Human Connectome Project dataset primarily consists of data from healthy participants, with varying levels of depression scores.

CONCLUSION

These results suggest that the habenula may facilitate depression by suppressing dopamine reward centers, and ketamine may relieve depression by disinhibiting these dopaminergic regions. This study may enhance our understanding of the neural underpinnings of depression and ketamine's antidepressant effects.

AKAP150-anchored PKA regulation of synaptic transmission and plasticity, neuronal excitability and CRF neuromodulation in the lateral habenula

Numerous studies of hippocampal synaptic function in learning and memory have established the functional significance of the scaffolding A-kinase anchoring protein 150 (AKAP150) in kinase and phosphatase regulation of synaptic receptor and ion channel trafficking/function and hence synaptic transmission/plasticity, and neuronal excitability. Emerging evidence also suggests that AKAP150 signaling may play a critical role in brain's processing of rewarding/aversive experiences. Here we focused on an unexplored role of AKAP150 in the lateral habenula (LHb), a diencephalic brain region that integrates and relays negative reward signals from forebrain striatal and limbic structures to midbrain monoaminergic centers. LHb aberrant activity (specifically hyperactivity) is also linked to depression. Using whole cell patch clamp recordings in LHb of male wildtype (WT) and ΔPKA knockin mice (with deficiency in AKAP-anchoring of PKA), we found that the genetic disruption of PKA anchoring to AKAP150 significantly reduced AMPA receptor (AMPAR)-mediated glutamatergic transmission and prevented the induction of presynaptic endocannabinoid (eCB)-mediated long-term depression (LTD) in LHb neurons. Moreover, ΔPKA mutation potentiated GABAA receptor (GABAAR)-mediated inhibitory transmission postsynaptically while increasing LHb intrinsic neuronal excitability through suppression of medium afterhyperpolarizations (mAHPs). Given that LHb is a highly stress-responsive brain region, we further tested the effects of corticotropin releasing factor (CRF) stress neuromodulator on synaptic transmission and intrinsic excitability of LHb neurons in WT and ΔPKA mice. As in our earlier study in rat LHb, CRF significantly suppressed GABAergic transmission onto LHb neurons and increased intrinsic excitability by diminishing small-conductance potassium (SK) channel-mediated mAHPs. ΔPKA mutation-induced suppression of mAHPs also blunted the synaptic and neuroexcitatory actions of CRF in mouse LHb. Altogether, our data suggest that AKAP150 complex signaling plays a critical role in regulation of AMPAR and GABAAR synaptic strength, glutamatergic plasticity and CRF neuromodulation possibly through AMPAR and potassium channel trafficking and eCB signaling within the LHb.

Abnormal functional connectivity in the habenula is associated with subjective hyperarousal state in chronic insomnia disorder

Background

The hyperarousal process model plays a central role in the physiology of chronic insomnia disorder (CID). Recent evidence has demonstrated that the habenula is involved in the arousal and sleep-wake cycle. However, whether the intrinsic habenular functional network contributes to the underlying mechanism of CID and its relationship to the arousal state in CID remains unclear.

Methods

This single-centered study included 34 patients with subjective CID and 22 matched good sleep control (GSC), and underwent a series of neuropsychological tests and resting-state functional magnetic resonance imaging scans. The habenular functional network was assessed using seed-based functional connectivity (FC) analysis. The subjective arousal state was evaluated with the hyperarousal scale (HAS). Alterations in the habenular FC network and their clinical significance in patients with CID were explored.

Results

Compared with the GSC group, the CID group showed decreased habenular FC in the left caudate nucleus and right inferior parietal lobule and increased FC in the right habenula, bilateral calcarine cortex, and posterior cingulate cortex. The decreased FC between the left habenula and caudate nucleus was associated with an increased arousal state in the CID group.

Conclusion

The present results provide evidence for a dysfunctional habenular network in patients with CID. These findings extend our understanding of the neuropathological mechanisms underlying the hyperarousal model in chronic insomnia.

Neurobehavioral impairments predict specific cerebral damage in rat model of subarachnoid hemorrhage

Subarachnoid hemorrhage (SAH) is a severe form of stroke that can cause unpredictable and diffuse cerebral damage, which is difficult to detect until it becomes irreversible. Therefore, there is a need for a reliable method to identify dysfunctional regions and initiate treatment before permanent damage occurs. Neurobehavioral assessments have been suggested as a possible tool to detect and approximately localize dysfunctional cerebral regions. In this study, we hypothesized that a neurobehavioral assessment battery could be a sensitive and specific early warning for damage in discrete cerebral regions following SAH. To test this hypothesis, a behavioral battery was employed at multiple time points after SAH induced via an endovascular perforation, and brain damage was confirmed via postmortem histopathological analysis. Our results demonstrate that impairment of sensorimotor function accurately predict damage in the cerebral cortex (AUC: 0.905; sensitivity: 81.8%; specificity: 90.9%) and striatum (AUC: 0.913; sensitivity: 90.1%; specificity: 100%), while impaired novel object recognition is a more accurate indicator of damage to the hippocampus (AUC: 0.902; sensitivity: 74.1%; specificity: 83.3%) than impaired reference memory (AUC: 0.746; sensitivity: 72.2%; specificity: 58.0%). Tests for anxiety-like and depression-like behaviors predict damage to the amygdala (AUC: 0.900; sensitivity: 77.0%; specificity: 81.7%) and thalamus (AUC: 0.963; sensitivity: 86.3%; specificity: 87.8%), respectively. This study suggests that recurring behavioral testing can accurately predict damage in specific brain regions, which could be developed into a clinical battery for early detection of SAH damage in humans, potentially improving early treatment and outcomes.

Resting-state functional connectivity disruption between the left and right pallidum as a biomarker for subthreshold depression

Although the identification of late adolescents with subthreshold depression (StD) may provide a basis for developing effective interventions that could lead to a reduction in the prevalence of StD and prevent the development of major depressive disorder, knowledge about the neural basis of StD remains limited. The purpose of this study was to develop a generalizable classifier for StD and to shed light on the underlying neural mechanisms of StD in late adolescents. Resting-state functional magnetic resonance imaging data of 91 individuals (30 StD subjects, 61 healthy controls) were included to build an StD classifier, and eight functional connections were selected by using the combination of two machine learning algorithms. We applied this biomarker to an independent cohort (n = 43) and confirmed that it showed generalization performance (area under the curve = 0.84/0.75 for the training/test datasets). Moreover, the most important functional connection was between the left and right pallidum, which may be related to clinically important dysfunctions in subjects with StD such as anhedonia and hyposensitivity to rewards. Investigation of whether modulation of the identified functional connections can be an effective treatment for StD may be an important topic of future research.

Multimodality neuroimaging in vascular mild cognitive impairment: A narrative review of current evidence

The vascular mild cognitive impairment (VaMCI) is generally accepted as the premonition stage of vascular dementia (VaD). However, most studies are focused mainly on VaD as a diagnosis in patients, thus neglecting the VaMCI stage. VaMCI stage, though, is easily diagnosed by vascular injuries and represents a high-risk period for the future decline of patients’ cognitive functions. The existing studies in China and abroad have found that magnetic resonance imaging technology can provide imaging markers related to the occurrence and development of VaMCI, which is an important tool for detecting the changes in microstructure and function of VaMCI patients. Nevertheless, most of the existing studies evaluate the information of a single modal image. Due to the different imaging principles, the data provided by a single modal image are limited. In contrast, multi-modal magnetic resonance imaging research can provide multiple comprehensive data such as tissue anatomy and function. Here, a narrative review of published articles on multimodality neuroimaging in VaMCI diagnosis was conducted，and the utilization of certain neuroimaging bio-markers in clinical applications was narrated. These markers include evaluation of vascular dysfunction before tissue damages and quantification of the extent of network connectivity disruption. We further provide recommendations for early detection, progress, prompt treatment response of VaMCI, as well as optimization of the personalized treatment plan.

Higher betweenness and degree centrality in the frontal and cerebellum cortex of Meige's syndrome patients than hemifacial spasm patients

Meige's syndrome and hemifacial spasm (HFS) are two different forms of dystonic movement disorder, but their difference in terms of resting state functional connectivity (rsFC) remains unclear. The present study applied resting state fMRI on the patients and quantified their functional connectivity with graph theoretical measures, including the degree centrality and the betweenness centrality. Fifteen Meige's syndrome patients and 19 HFS patients matched in age and gender were recruited and their MRI data were collected. To analyze the rsFC, we adopted the Anatomical Automatic Labeling (AAL) template, a brain atlas system including 90 regions of interest (ROIs) covering all the brain regions of cerebral cortex. For each participant, the time-course of each ROI was extracted, and the corresponding degree centrality and betweenness centrality of each ROI were computed. These measures were then compared between the Meige's syndrome patients and the HFS patients. Meige's syndrome patients showed higher betweenness centrality and degree centrality of bilateral superior medial frontal cortex, the left cerebellum cortex, etc. than the HFS patients. Our results suggest that the rsFC pattern in Meige's syndrome patients might become more centralized toward the prefrontal and vestibular cerebellar systems, indicating less flexibility in their functional connections. These results preliminarily revealed the characteristic abnormality in the functional connection of Meige's patients and may help to explore better treatment.

Modification and Expression of mRNA m6A in the Lateral Habenular of Rats after Long-Term Exposure to Blue Light during the Sleep Period

Artificial lighting, especially blue light, is becoming a public-health risk. Excessive exposure to blue light at night has been reported to be associated with brain diseases. However, the mechanisms underlying neuropathy induced by blue light remain unclear. An early anatomical tracing study described the projection of the retina to the lateral habenula (LHb), whereas more mechanistic reports are available on multiple brain functions and neuropsychiatric disorders in the LHb, which are rarely seen in epigenetic studies, particularly N6-methyladenosine (m6A). The purpose of our study was to first expose Sprague-Dawley rats to blue light (6.11 ± 0.05 mW/cm2, the same irradiance as 200 lx of white light in the control group) for 4 h, and simultaneously provide white light to the control group for the same time to enter a sleep period. The experiment was conducted over 12 weeks. RNA m6A modifications and different mRNA transcriptome profiles were observed in the LHb. We refer to this experimental group as BLS. High-throughput MeRIP-seq and mRNA-seq were performed, and we used bioinformatics to analyze the data. There were 188 genes in the LHb that overlapped between differentially m6A-modified mRNA and differentially expressed mRNA. The Kyoto Encyclopedia of Genes and Genomes and gene ontology analysis were used to enrich neuroactive ligand-receptor interaction, long-term depression, the cyclic guanosine monophosphate-dependent protein kinase G (cGMP-PKG) signaling pathway, and circadian entrainment. The m6A methylation level of the target genes in the BLS group was disordered. In conclusion, this study suggests that the mRNA expression and their m6A of the LHb were abnormal after blue light exposure during the sleep period, and the methylation levels of target genes related to synaptic plasticity were disturbed. This study offers a theoretical basis for the scientific use of light.

Aberrant degree centrality of functional brain networks in subclinical depression and major depressive disorder

BACKGROUND

As one of the most common diseases, major depressive disorder (MDD) has a significant adverse impact on the li of patients. As a mild form of depression, subclinical depression (SD) serves as an indicator of progression to MDD. This study analyzed the degree centrality (DC) for MDD, SD, and healthy control (HC) groups and identified the brain regions with DC alterations.

METHODS

The experimental data were composed of resting-state functional magnetic resonance imaging (rs-fMRI) from 40 HCs, 40 MDD subjects, and 34 SD subjects. After conducting a one-way analysis of variance, two-sample t-tests were used for further analysis to explore the brain regions with changed DC. Receiver operating characteristic (ROC) curve analysis of single index and composite index features was performed to analyze the distinguishable ability of important brain regions.

RESULTS

For the comparison of MDD vs. HC, increased DC was found in the right superior temporal gyrus (STG) and right inferior parietal lobule (IPL) in the MDD group. For SD vs. HC, the SD group showed a higher DC in the right STG and the right middle temporal gyrus (MTG), and a smaller DC in the left IPL. For MDD vs. SD, increased DC in the right middle frontal gyrus (MFG), right IPL, and left IPL, and decreased DC in the right STG and right MTG was found in the MDD group. With an area under the ROC (AUC) of 0.779, the right STG could differentiate MDD patients from HCs and, with an AUC of 0.704, the right MTG could differentiate MDD patients from SD patients. The three composite indexes had good discriminative ability in each pairwise comparison, with AUCs of 0.803, 0.751, and 0.814 for MDD vs. HC, SD vs. HC, and MDD vs. SD, respectively.

CONCLUSION

Altered DC in the STG, MTG, IPL, and MFG were identified in depression groups. The DC values of these altered regions and their combinations presented good discriminative ability between HC, SD, and MDD. These findings could help to find effective biomarkers and reveal the potential mechanisms of depression.

Common and unique neural activities in subclinical depression and major depressive disorder indicate the development of brain impairments in different depressive stages

BACKGROUND

Subclinical depression (SD) and major depressive disorder (MDD) can be considered as the early and late stages of depression, but the characteristics of intrinsic neural activity in different depressive stages are largely unknown.

METHODS

Twenty-six SD, 36 MDD subjects and 33 well-matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Voxel-wise regional homogeneity (ReHo) was analyzed to explore the alterations of intrinsic neural activity, and machine learning classification based on ReHo features was performed to assess potential performance for diagnostic classification.

RESULTS

Common alterations of ReHo in both SD and MDD groups were found in the bilateral middle temporal gyrus and the left middle occipital gyrus. Opposite alterations in SD and MDD groups were found in the right superior cerebellum. Moreover, increased ReHo in the bilateral precuneus was only found in MDD, while increased ReHo in the right middle frontal gyrus and precentral gyrus were unique to SD. The distinct ReHo values correctly identified SD, MDD, and HC by linear support vector machine (SVM) with an accuracy of 77.89 %, which further verified the discrimination ability of altered ReHo in these brain regions.

LIMITATION

The sample size is relatively small.

CONCLUSION

Common and unique ReHo alterations provided insights into the development of brain impairments in depression, and helped to understand the pathophysiology of SD and MDD.

Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning

Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.

Alterations of functional connectivity of the lateral habenula in subclinical depression and major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a common cause of disability and morbidity, affecting about 10% of the population worldwide. Subclinical depression (SD) can be understood as a precursor of MDD, and therefore provides an MDD risk indicator. The pathogenesis of MDD and SD in humans is still unclear, and the current diagnosis lacks accurate biomarkers and gold standards.

METHODS

A total of 40 MDD, 34 SD, and 40 healthy control (HC) participants matched by age, gender, and education were included in this study. Resting-state functional magnetic resonance images (rs-fMRI) were used to analyze the functional connectivity (FC) of the posterior parietal thalamus (PPtha), which includes the lateral habenula, as the region of interest. Analysis of variance with the post hoc t-test test was performed to find significant differences in FC and clarify the variations in FC among the HC, SD, and MDD groups.

RESULTS

Increased FC was observed between PPtha and the left inferior temporal gyrus (ITG) for MDD versus SD, and between PPtha and the right ITG for SD versus HC. Conversely, decreased FC was observed between PPtha and the right middle temporal gyrus (MTG) for MDD versus SD and MDD versus HC. The FC between PPtha and the middle frontal gyrus (MFG) in SD was higher than that in MDD and HC. Compared with the HC group, the FC of PPtha-ITG (left and right) increased in both the SD and MDD groups, PPtha-MTG (right) decreased in both the SD and MDD groups and PPtha-MFG (right) increased in the SD group and decreased in the MDD group.

CONCLUSION

Through analysis of FC measured by rs-fMRI, the altered FC between PPtha and several brain regions (right and left ITG, right MTG, and right MFG) has been identified in participants with SD and MDD. Different alterations in FC between PPtha and these regions were identified for patients with depression. These findings might provide insights into the potential pathophysiological mechanisms of SD and MDD, especially related to PPtha and the lateral habenula.

Laterality and Sex Differences of Human Lateral Habenula Afferent and Efferent Fiber Tracts

Introduction

The lateral habenula (LHb) is an epithalamic nucleus associated with negative valence and affective disorders. It receives input via the stria medullaris (SM) and sends output via the fasciculus retroflexus (FR). Here, we use tractography to reconstruct and characterize this pathway.

Methods

Multi-shell human diffusion magnetic resonance imaging (dMRI) data was obtained from the human connectome project (HCP) (n = 20, 10 males) and from healthy controls (n = 10, 6 males) scanned at our institution. We generated LHb afferents and efferents using probabilistic tractography by selecting the pallidum as the seed region and the ventral tegmental area as the output target.

Results

We were able to reconstruct the intended streamlines in all individuals from the HCP dataset and our dataset. Our technique also aided in identification of the LHb. In right-handed individuals, the streamlines were significantly more numerous in the left hemisphere (mean ratio 1.59 ± 0.09, p = 0.04). In left-handed individuals, there was no hemispheric asymmetry on average (mean ratio 1.00 ± 0.09, p = 1.0). Additionally, these streamlines were significantly more numerous in females than in males (619.9 ± 159.7 vs. 225.9 ± 66.03, p = 0.04).

Conclusion

We developed a method to reconstruct the SM and FR without manual identification of the LHb. This technique enables targeting of these fiber tracts as well as the LHb. Furthermore, we have demonstrated that there are sex and hemispheric differences in streamline number. These findings may have therapeutic implications and warrant further investigation.

Abnormal habenula functional connectivity characterizes treatment-resistant depression

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Habenular hyper connectivity characterizes treatment-resistant depression.

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An interplay between reward and default mode networks is linked to suicidality.

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Abnormal habenula connectivity is a possible mechanism for anhedonia.

Background

A significant proportion of patients with major depressive disorder are resistant to antidepressant medication and psychological treatments. A core symptom of treatment-resistant depression (TRD) is anhedonia, or the inability to feel pleasure, which has been attributed to disrupted habenula function – a component of the reward network. This study aimed to map detailed neural circuitry architecture related to the habenula to identify neural mechanisms of TRD.

Methods

35 TRD patients, 35 patients with treatment-sensitive depression (TSD), and 38 healthy controls (HC) underwent resting-state functional magnetic resonance imaging. Functional connectivity analyses were performed using the left and right habenula as seed regions of interest, and the three groups were compared using whole-brain voxel-wise comparisons.

Results

The TRD group demonstrated hyperconnectivity of the left habenula to the left precuneus cortex and the right precentral gyrus, compared to the TSD group, and to the right precuneus cortex, compared to the TSD and HC groups. In contrast, TSD demonstrated hypoconnectivity than HC for both connectivity measures. These connectivity values were significantly higher in patients with a history of suicidal ideation.

Conclusions

This study provides evidence that, unlike TSD, TRD is characterized by hyperconnectivity of the left habenula particularly with regions of the default mode network. An increased interplay between reward and default mode networks is linked to suicidality and could be a possible mechanism for anhedonia in hard to treat depression.

Functional connectivity between the habenula and default mode network and its association with the antidepressant effect of ketamine

BACKGROUND

Recently, an animal model for depression has shown that ketamine, an N-methyl- d-aspartate receptor (NMDAR) antagonist, elicits a rapid-acting antidepressant effect by blocking NMDAR-dependent bursting in the lateral habenula (Hb). However, evidence from human studies remains scarce.

METHODS

This study explored the changes of resting-state functional connectivity (FC) of the Hb in responders and nonresponders who was diagnosed with unipolar or bipolar depression before and after ketamine treatment. The response was defined as a ≥50% reduction in the total MADRS score at Day 13 (24 h following the sixth infusion) in comparison with the baseline score. Correlation analyses were performed to identify an association between symptom improvement and the signals of the significantly different brain regions detected in the above imaging analysis.

RESULTS

In the post-hoc region-of-interest analysis, an enhanced baseline FC between Hb and several hubs of the default mode network (including angulate cortex, precuneus, medial prefrontal cortex, and middle temporal cortex) was observed in responders (≥50% decrease in the Montgomery-Asberg Scale at 2 weeks) compared with nonresponders.

CONCLUSIONS

These pilot findings may suggest a potential neural mechanism by which ketamine exerts its robust antidepressant efficacy via downregulation of aberrant habenular FC with parts of the default mode network.

Functional Brain Connections Identify Sensorineural Hearing Loss and Predict the Outcome of Cochlear Implantation

Identification of congenital sensorineural hearing loss (SNHL) and early intervention, especially by cochlear implantation (CI), are crucial for restoring hearing in patients. However, high accuracy diagnostics of SNHL and prognostic prediction of CI are lacking to date. To diagnose SNHL and predict the outcome of CI, we propose a method combining functional connections (FCs) measured by functional magnetic resonance imaging (fMRI) and machine learning. A total of 68 children with SNHL and 34 healthy controls (HC) of matched age and gender were recruited to construct classification models for SNHL and HC. A total of 52 children with SNHL that underwent CI were selected to establish a predictive model of the outcome measured by the category of auditory performance (CAP), and their resting-state fMRI images were acquired. After the dimensional reduction of FCs by kernel principal component analysis, three machine learning methods including the support vector machine, logistic regression, and k-nearest neighbor and their voting were used as the classifiers. A multiple logistic regression method was performed to predict the CAP of CI. The classification model of voting achieves an area under the curve of 0.84, which is higher than that of three single classifiers. The multiple logistic regression model predicts CAP after CI in SNHL with an average accuracy of 82.7%. These models may improve the identification of SNHL through fMRI images and prognosis prediction of CI in SNHL.

Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data

BACKGROUND

Given that major depressive disorder (MDD) is both biologically and clinically heterogeneous, a diagnostic system integrating neurobiological markers and clinical characteristics would allow for better diagnostic accuracy and, consequently, treatment efficacy.

OBJECTIVE

Our study aimed to evaluate the discriminative and predictive ability of unimodal, bimodal, and multimodal approaches in a total of seven machine learning (ML) models-clinical, demographic, functional near-infrared spectroscopy (fNIRS), combinations of two unimodal models, as well as a combination of all three-for MDD.

METHODS

We recruited 65 adults with MDD and 68 matched healthy controls, who provided both sociodemographic and clinical information, and completed the HAM-D questionnaire. They were also subject to fNIRS measurement when participating in the verbal fluency task. Using the nested cross validation procedure, the classification performance of each ML model was evaluated based on the area under the receiver operating characteristic curve (ROC), balanced accuracy, sensitivity, and specificity.

RESULTS

The multimodal ML model was able to distinguish between depressed patients and healthy controls with the highest balanced accuracy of 87.98 ± 8.84% (AUC = 0.92; 95% CI (0.84-0.99) when compared with the uni- and bi-modal models.

CONCLUSIONS

Our multimodal ML model demonstrated the highest diagnostic accuracy for MDD. This reinforces the biological and clinical heterogeneity of MDD and highlights the potential of this model to improve MDD diagnosis rates. Furthermore, this model is cost-effective and clinically applicable enough to be established as a robust diagnostic system for MDD based on patients' biosignatures.

Circadian Influences on the Habenula and Their Potential Contribution to Neuropsychiatric Disorders

The neural circadian system consists of the master circadian clock in the hypothalamic suprachiasmatic nuclei (SCN) communicating time of day cues to the rest of the body including other brain areas that also rhythmically express circadian clock genes. Over the past 16 years, evidence has emerged to indicate that the habenula of the epithalamus is a candidate extra-SCN circadian oscillator. When isolated from the SCN, the habenula sustains rhythms in clock gene expression and neuronal activity, with the lateral habenula expressing more robust rhythms than the adjacent medial habenula. The lateral habenula is responsive to putative SCN output factors as well as light information conveyed to the perihabenula area. Neuronal activity in the lateral habenula is altered in depression and intriguingly disruptions in circadian rhythms can elevate risk of developing mental health disorders including depression. In this review, we will principally focus on how circadian and light signals affect the lateral habenula and evaluate the possibility that alteration in these influences contribute to mental health disorders.

Discriminating subclinical depression from major depression using multi-scale brain functional features: A radiomics analysis

BACKGROUND

The diagnosis of subclinical depression (SD) currently relies exclusively on subjective clinical scores and structured interviews, which shares great similarities with major depression (MD) and increases the risk of misdiagnosis of SD and MD. This study aimed to develop a method of disease classification for SD and MD by resting-state functional features using radiomics strategy.

METHODS

Twenty-six SD, 36 MD subjects and 33 well-matched healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). A novel radiomics analysis was proposed to discriminate SD from MD. Multi-scale brain functional features were extracted to explore a comprehensive representation of functional characteristics. A two-level feature selection strategy and support vector machine (SVM) were employed for classification.

RESULTS

The overall classification accuracy among SD, MD and HC groups was 84.21%. Particularly, the model excellently distinguished SD from MD with 96.77% accuracy, 100% sensitivity, and 92.31% specificity. Moreover, features with high discriminative power to distinguish SD from MD showed a strong association with default mode network, frontoparietal network, affective network, and visual network regions.

LIMITATION

The sample size was relatively small, which may limit the application in clinical translation to some extent.

CONCLUSION

These findings demonstrated that a valid radiomics approach based on functional measures can discriminate SD from MD with a high classification performance, facilitating an objective and reliable diagnosis individually in clinical practice. Features with high discriminative power may provide insight into a profound understanding of the brain functional impairments and pathophysiology of SD and MD.

Multi-level variations of lateral habenula in depression: A comprehensive review of current evidence

Despite extensive research in recent decades, knowledge of the pathophysiology of depression in neural circuits remains limited. Recently, the lateral habenula (LHb) has been extensively reported to undergo a series of adaptive changes at multiple levels during the depression state. As a crucial relay in brain networks associated with emotion regulation, LHb receives excitatory or inhibitory projections from upstream brain regions related to stress and cognition and interacts with brain regions involved in emotion regulation. A series of pathological alterations induced by aberrant inputs cause abnormal function of the LHb, resulting in dysregulation of mood and motivation, which present with depressive-like phenotypes in rodents. Herein, we systematically combed advances from rodents, summarized changes in the LHb and related neural circuits in depression, and attempted to analyze the intrinsic logical relationship among these pathological alterations. We expect that this summary will greatly enhance our understanding of the pathological processes of depression. This is advantageous for fostering the understanding and screening of potential antidepressant targets against LHb.

Individual prediction of hemispheric similarity of functional connectivity during normal aging

In the aging process of normal people, the functional activity pattern of brain is in constant change, and the change of brain runs through the whole life cycle, which plays a crucial role in the track of individual development. In recent years, some studies had been carried out on the brain functional activity pattern during individual aging process from different perspectives, which provided an opportunity for the problem we want to study. In this study, we used the resting-state functional magnetic resonance imaging (rs-fMRI) data from Cambridge Center for Aging and Neuroscience (Cam-CAN) database with large sample and long lifespan, and computed the functional connectivity (FC) values for each individual. Based on these values, the hemispheric similarity of functional connectivity (HSFC) obtained by Pearson correlation was used as the starting point of this study. We evaluated the ability of individual recognition of HSFC in the process of aging, as well as the variation trend with aging process. The results showed that HSFC could be used to identify individuals effectively, and it could reflect the change rule in the process of aging. In addition, we observed a series of results at the sub-module level and find that the recognition rate in the sub-module was different from each other, as well as the trend with age. Finally, as a validation, we repeated the main results by human brainnetome atlas (BNA) template and without global signal regression, found that had a good robustness. This also provides a new clue to hemispherical change patterns during normal aging.

Characterization of brain functional connectivity in treatment-resistant depression

OBJECTIVE

To characterize the functional connectivity (FC) of target brain regions for deep brain stimulation (DBS) in patients with treatment-resistant depression (TRD), and to evaluate its gender and brain lateralization dependence.

METHODS

Thirty-one TRD patients and twenty-nine healthy control (HC) subjects participated. FC of subcallosal cingulate gyrus (SCG), ventral caudate (VCa), nucleus accumbens (NAc), lateral habenula (LHb), and inferior thalamic peduncle (ITP) were evaluated using resting-state fMRI. FC was characterized by calculating the nodal 'degree', a major feature of the graph theory.

RESULTS

The degree measures of the left and right VCa, the left LHb, and the left ITP were significantly greater in the TRD than in the HC group. The degree was greater in females with TRD in all these regions except the right LHb. Finally, the left hemisphere was generally more affected by depression and presented significant degrees in LHb and ITP regions of the patients.

CONCLUSION

Our findings demonstrate the ability of degree to characterize brain FC and identify the regions with abnormal activities in TRD patients. This implies that the degree may have the potential to be used as an important graph-theoretical feature to further investigate the mechanisms underlying TRD, and consequently along with other diagnostic markers, to assist in the determination of the appropriate target region for DBS treatment in TRD patients.

TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis

Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Graph convolutional networks (GCNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GCNs model for brain networks faces several challenges, including high dimensional and noisy correlation in the brain networks, limited labeled training data, and depth limitation of GCN learning. Generalization and interpretability are important in developing predictive models for clinical diagnosis. To address these challenges, we proposed an ensemble framework involving hierarchical GCN and transfer learning for sparse brain networks, which allows GCN to capture the intrinsic correlation among the subjects and domains, to improve the network embedding learning for disease diagnosis. Extensive experiments on two real medical clinical applications: diagnosis of Autism spectrum disorder (ASD) and diagnosis of Alzheimer's disease (AD) on both the ADNI and ABIDE databases, showing the effectiveness of the proposed framework. We achieved state-of-the-art accuracy and AUC for AD/MCI and ASD/NC (Normal control) classification in comparison with studies that used functional connectivity as features or GCN models. The proposed TE-HI-GCN model achieves the best classification performance, leading to about 27.93% (31.38%) improvement for ASD and 16.86% (44.50%) for AD in terms of accuracy and AUC compared with the traditional GCN model. Moreover, the obtained clustering results show high correspondence with the previous neuroimaging derived evidence of within and between-networks biomarkers for ASD. The discovered subnetworks are used as evidence for the proposed TE-HI-GCN model. Furthermore, this work is the first attempt of transfer learning on the two related disorder domains to uncover the correlation among the two diseases with a transfer learning scheme.

Altered Degree Centrality of Brain Networks in Parkinson's Disease With Freezing of Gait: A Resting-State Functional MRI Study

Freezing of gait (FOG) in Parkinson's disease (PD) leads to devastating consequences; however, little is known about its functional brain network. We explored the differences in degree centrality (DC) of functional networks among PD with FOG (PD FOG+), PD without FOG (PD FOG–), and healthy control (HC) groups. In all, 24 PD FOG+, 37 PD FOG–, and 22 HCs were recruited and their resting-state functional magnetic imaging images were acquired. The whole brain network was analyzed using graph theory analysis. DC was compared among groups using the two-sample t-test. The DC values of disrupted brain regions were correlated with the FOG Questionnaire (FOGQ) scores. Receiver operating characteristic curve analysis was performed. We found significant differences in DC among groups. Compared with HCs, PD FOG+ patients showed decreased DC in the middle frontal gyrus (MFG), superior temporal gyrus (STG), parahippocampal gyrus (PhG), inferior temporal gyrus (ITG), and middle temporal gyrus (MTG). Compared with HC, PD FOG– presented with decreased DC in the MFG, STG, PhG, and ITG. Compared with PD FOG–, PD FOG+ showed decreased DC in the MFG and ITG. A negative correlation existed between the DC of ITG and FOGQ scores; the DC in ITG could distinguish PD FOG+ from PD FOG– and HC. The calculated AUCs were 81.3, 89.5, and 77.7% for PD FOG+ vs. HC, PD FOG– vs. HC, and PD FOG+ vs. PD FOG–, respectively. In conclusion, decreased DC of ITG in PD FOG+ patients compared to PD FOG– patients and HCs may be a unique feature for PD FOG+ and can likely distinguish PD FOG+ from PD FOG– and HC groups.

A lateralized model of the pain-depression dyad

Chronic pain and depression are two frequently co-occurring and debilitating conditions. Even though the former is treated as a physical affliction, and the latter as a mental illness, both disorders closely share neural substrates. Here, we review the association of pain with depression, especially when symptoms are lateralized on either side of the body. We also explore the overlapping regions in the forebrain implicated in these conditions. Finally, we synthesize these findings into a model, which addresses gaps in our understanding of comorbid pain and depression. Our lateralized pain-depression dyad model suggests that individuals diagnosed with depression should be closely monitored for pain symptoms in the left hemibody. Conversely, for patients in pain, with the exception of acute pain with a known source, referrals in today's pain centers for psychological evaluation should be part of standard practice, within the framework of an interdisciplinary approach to pain treatment.

The Role of the Lateral Habenula in Inhibitory Learning from Reward Omission

The lateral habenula (LHb) is a phylogenetically primitive brain structure that plays a key role in learning to inhibit distinct responses to specific stimuli. This structure is activated by primary aversive stimuli, cues predicting an imminent aversive event, unexpected reward omissions, and cues associated with the omission of an expected reward. The most widely described physiological effect of LHb activation is acutely suppressing midbrain dopaminergic signaling. However, recent studies have identified multiple means by which the LHb promotes this effect as well as other mechanisms of action. These findings reveal the complex nature of LHb circuitry. The present paper reviews the role of this structure in learning from reward omission. We approach this topic from the perspective of computational models of behavioral change that account for inhibitory learning to frame key findings. Such findings are drawn from recent behavioral neuroscience studies that use novel brain imaging, stimulation, ablation, and reversible inactivation techniques. Further research and conceptual work are needed to clarify the nature of the mechanisms related to updating motivated behavior in which the LHb is involved. As yet, there is little understanding of whether such mechanisms are parallel or complementary to the well-known modulatory function of the more recently evolved prefrontal cortex.

Altered spontaneous neural activity in the precuneus, middle and superior frontal gyri, and hippocampus in college students with subclinical depression

BACKGROUND

Subclinical depression (ScD) is a prevalent condition associated with relatively mild depressive states, and it poses a high risk of developing into major depressive disorder (MDD). However, the neural pathology of ScD is still largely unknown. Identifying the spontaneous neural activity involved in ScD may help clarify risk factors for MDD and explore treatment strategies for mild stages of depression.

METHODS

A total of 34 ScD subjects and 40 age-, sex-, and education-matched healthy controls were screened from 1105 college students. The amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) of resting-state fMRI were calculated to reveal neural activity. Strict statistical strategies, including Gaussian random field (GRF), false discovery rate (FDR), and permutation test (PT) with threshold-free cluster enhancement (TFCE), were conducted. Based on the altered ALFF and ReHo, resting-state functional connectivity (RSFC) was further analyzed using a seed-based approach.

RESULTS

The right precuneus and left middle frontal gyrus (MFG) both showed significantly increased ALFF and ReHo in ScD subjects. Moreover, the left hippocampus and superior frontal gyrus (SFG) showed decreased ALFF and increased ReHo, respectively. In addition, ScD subjects showed increased RSFC between MFG and hippocampus compared to healthy controls, and significant positive correlation was found between the Beck Depression Inventory-II (BDI-II) score and RSFC from MFG to hippocampus in ScD group.

CONCLUSION

Spontaneous neural activities in the right precuneus, left MFG, SFG, and hippocampus were altered in ScD subjects. Functional alterations in these dorsolateral prefrontal cortex and default mode network regions are largely related to abnormal emotional processing in ScD, and indicate strong associations with brain impairments in MDD, which provide insight into potential pathophysiology mechanisms of subclinical depression.

The Time to Change for Mental Health and Wellbeing via Virtual Professional Coaching: Longitudinal Observational Study

BACKGROUND

Optimal mental health yields many benefits and reduced costs to employees and organizations; however, the workplace introduces challenges to building and maintaining mental health that affects wellbeing. While many organizations have introduced programming to aid employee mental health and wellbeing, the uptake and effectiveness of these efforts vary. One barrier to developing more effective interventions is a lack of understanding about how to improve wellbeing over time. The current study examined not only whether employer-provided coaching is an effective strategy to improve mental health and wellbeing in employees; but also how this intervention changes wellbeing in stages over time.

OBJECTIVE

The goal of this study was to determine whether BetterUp, a longitudinal one-on-one virtual coaching intervention, improves components of mental health and psychological wellbeing and whether the magnitude of changes vary in stages over time. This is the first research study to evaluate the effectiveness of professional coaching through three repeated assessments, moving beyond a pre- to post-intervention design. The outcomes of this study will enable coaches and employers to design more targeted interventions by outlining when to expect maximal growth in specific outcomes throughout the coaching engagement.

METHODS

Three identical assessments were completed by 391 users of BetterUp - prior to the start of coaching, after approximately 3-4 months of coaching, and again after 6-7 months of coaching. Three scales were used to evaluate psychological and behavioral dimensions that support management of mental health - stress management, resilience, and life satisfaction. Six additional scales assessed psychological wellbeing - emotional regulation, prospection ability, finding purpose and meaning, self-awareness, self-efficacy, and social connection.

RESULTS

Using mixed effects modeling, varying rates of change were observed in several dimensions of mental health and psychological wellbeing. Initial rapid improvements in the first half of the intervention, followed by slower growth in the second half of the intervention, were found for prospection ability, self-awareness, self-efficacy, social connection, emotional regulation, and a reduction in stress (range of unstandardized βs for each assessment: .10-.19). Life satisfaction improved continuously throughout the full intervention period (β: .13). Finding purpose in meaning at work and building resilience both grew continuously throughout the coaching intervention, but larger gains were experienced in the second half of the intervention (βs: .08-.18), requiring the full length of the intervention to realize maximal growth.

CONCLUSIONS

The results demonstrate the effectiveness of the BetterUp virtual one-on-one coaching to improve psychological wellbeing, while mitigating threats to mental health such as excessive and prolonged stress, low resilience, and poor satisfaction with life. The improvements across the collection of outcomes were time-dependent and provide important insights to users and practitioners about how and when to expect maximal improvements in a range of interrelated personal and professional outcomes.

CLINICALTRIAL

Integrating Structural and Functional Interhemispheric Brain Connectivity of Gait Freezing in Parkinson's Disease

Freezing of gait (FOG) has devastating consequences for patients with Parkinson's disease (PD), but the underlying pathophysiological mechanism is unclear. This was investigated in the present study by integrated structural and functional connectivity analyses of PD patients with or without FOG (PD FOG+ and PD FOG-, respectively) and healthy control (HC) subjects. We performed resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging of 24 PD FOG+ patients, 37 PD FOG- patients, and 24 HCs. Tract-based spatial statistics was applied to identify white matter (WM) abnormalities across the whole brain. Fractional anisotropy (FA) and mean diffusivity (MD) of abnormal WM areas were compared among groups, and correlations between these parameters and clinical severity as determined by FOG Questionnaire (FOGQ) score were analyzed. Voxel-mirrored homotopic connectivity (VMHC) was calculated to identify brain regions with abnormal interhemispheric connectivity. Structural and functional measures were integrated by calculating correlations between VMHC and FOGQ score and between FA, MD, and VMHC. The results showed that PD FOG+ and PD FOG- patients had decreased FA in the corpus callosum (CC), cingulum (hippocampus), and superior longitudinal fasciculus and increased MD in the CC, internal capsule, corona radiata, superior longitudinal fasciculus, and thalamus. PD FOG+ patients had more WM abnormalities than PD FOG- patients. FA and MD differed significantly among the splenium, body, and genu of the CC in all three groups (P < 0.05). The decreased FA in the CC was positively correlated with FOGQ score. PD FOG+ patients showed decreased VMHC in the post-central gyrus (PCG), pre-central gyrus, and parietal inferior margin. In PD FOG+ patients, VMHC in the PCG was negatively correlated with FOGQ score but positively correlated with FA in CC. Thus, FOG is associated with impaired interhemispheric brain connectivity measured by FA, MD, and VMHC, which are related to clinical FOG severity. These results demonstrate that integrating structural and functional MRI data can provide new insight into the pathophysiological mechanism of FOG in PD.

ANALYSIS OF RS-FMRI IMAGES CLARIFIES BRAIN ALTERATIONS IN TYPE 2 DIABETES MELLITUS PATIENTS WITH COGNITIVE IMPAIRMENT

Type 2 Diabetes Mellitus (T2DM) increases the risk of cognitive impairment (CI); however, the underlying pathophysiological mechanisms are still not well understood. We propose to clarify the altered spontaneous brain activity and functional connectivity implicated in CI of T2DM by analyzing resting state functional MRI (rs-fMRI) data. Totally 22 T2DM patients with cognitive impairment (T2DM-CI) and 31 T2DM patients with normal cognition (T2DM-NC) are included in this study. The whole brain amplitude of low frequency fluctuation (ALFF) value, regional homogeneity (ReHo) value and functional connectivity (FC) analysis using posterior cingulate cortex (PCC) as a seed region are investigated through comparison between groups of T2DM-CI and T2DM-NC. It is found that, compared with T2DM-NC, T2DM-CI demonstrates the decreased ALFF in the regions of precuneus, posterior cingulate gyrus, middle occipital gyrus and left superior/middle frontal gyrus, but the increased ALFF in the left middle frontal gyrus and left superior temporal gyrus. In T2DM-CI, ReHo decreases in bilateral posterior cingulate gyrus, right precuneus, right inferior frontal gyrus, but increases in the middle frontal gyrus and right superior occipital gyrus. Higher FC between PCC and bilateral inferior parietal lobule and right middle/inferior frontal gyrus, lower FC between PCC and bilateral precuneus and right superior frontal gyrus are observed in T2DM-CI group. Compared with T2DM-NC, patients with T2DM-CI have presented altered ALFF, ReHo and FC in and between important brain regions. The observed alterations are thought to be implicated with cognitive impairment of T2DM as the potential imaging pathophysiological basis.

Circulating cytokines as predictors of depression in patients with breast cancer

Depression is a common comorbid disorder associated with breast cancer, and it can have considerable physical and psychological impacts. Circulating cytokines have been proposed as a potential tool to predict depression in various diseases; however, limited studies have specifically examined it in breast cancer. In this study, we examined and compared the prediction ability of various circulating cytokines for depression in patients with breast cancer. Eighty-three patients with a new diagnosis of breast cancer not receiving chemotherapy were recruited; among them, 15 patients had depression and 68 did not have depression. Depression was evaluated using the Patient Health Questionnaire 9 (PHQ-9). Cytokine levels in the serum were measured using an immunology multiplex assay. Two types of cytokines were assayed: (1) proinflammatory cytokines (interleukin [IL]-1β, IL-2, IL-6, IL-12, IL-17A, interferon [IFN]γ, and tumor necrosis factor [TNF]α) and (2) anti-inflammatory cytokines (IL-4, IL-5, IL-10, and IL-13). Receiver operating characteristic (ROC) analysis was performed to calculate the area under the curves (AUCs), sensitivities, and specificities of circulating cytokines for predicting depression. As a result, IL-2 (AUC = 0.78) and IL-5 (AUC = 0.76) demonstrated good predictability for depression, even after controlling for the covariates (i.e. age, education, stage of cancer, surgery, radiation therapy, and hormone therapy). The optimal cut-off value of IL-2 for predicting depression was 1.06 pg/mL with a sensitivity of 86.7% and a specificity of 52.9%; this cytokine also had the best prediction ability in this study. Owing to the prediction ability and practical feasibility of circulating cytokines, they may be used as a valid laboratory diagnostic tool for depression in breast cancer.

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity

To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n = 30; noHE, n = 32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary, DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

Less Is Better: Single-Digit Brain Functional Connections Predict T2DM and T2DM-Induced Cognitive Impairment

Type 2 diabetes mellitus (T2DM) leads to a higher risk of brain damage and adversely affects cognition. The underlying neural mechanism of T2DM-induced cognitive impairment (T2DM-CI) remains unclear. This study proposes to identify a small number of dysfunctional brain connections as imaging biomarkers, distinguishing between T2DM-CI, T2DM with normal cognition (T2DM-NC), and healthy controls (HC). We have recruited 22 T2DM-CI patients, 31 T2DM-NC patients, and 39 HCs. The structural Magnetic Resonance Imaging (MRI) and resting state fMRI images are acquired, and neuropsychological tests are carried out. Amplitude of low frequency fluctuations (ALFF) is analyzed to identify impaired brain regions implicated with T2DM and T2DM-CI. The functional network is built and all connections connected to impaired brain regions are selected. Subsequently, L₁-norm regularized sparse canonical correlation analysis and sparse logistic regression are used to identify discriminative connections and Support Vector Machine is trained to realize three two-category classifications. It is found that single-digit dysfunctional connections predict T2DM and T2DM-CI. For T2DM-CI versus HC, T2DM-NC versus HC, and T2DM-CI versus T2DM-NC, the number of connections is 6, 7, and 5 and the area under curve (AUC) can reach 0.912, 0.901, and 0.861, respectively. The dysfunctional connection is mainly related to Default Model Network (DMN) and long-distance links. The strength of identified connections is significantly different among groups and correlated with cognitive assessment score (p < 0.05). Via ALFF analysis and further feature selection algorithms, a small number of dysfunctional brain connections can be identified to predict T2DM and T2DM-CI. These connections might be the imaging biomarkers of T2DM-CI and targets of intervention.

Thalamo-Habenular Connection Differences Between Patients With Major Depressive Disorder and Normal Controls

Background: The thalamus and habenula are thought to be key brain regions in the etiology of major depressive disorder (MDD); however, few studies have investigated the structural connection between them. We compared the number of white matter tracts between the thalamus and habenula between patient with MDD and normal controls (NCs). Methods: The habenula and thalamus region of interest masks were extracted from brain magnetic resonance imaging data and individual tractography analysis was performed. First, we compared the number of fiber connections from the habenula to the thalamus between the MDD (n = 34) and NC (n = 37) groups and also compared hemispherical differences to investigate possible asymmetries. Results: There was a significant difference in the number of tracts in the right habenula-left mediodorsal thalamus pair between the two groups. For hemispherical fiber connections, the waytotal ratio of the right ipsilateral tract between the thalamus and habenula was significantly higher than that of the left ipsilateral tract in both groups. Conclusion: The number of right habenula-left mediodorsal thalamus tracts was higher in patients with MDD than in NCs. These results indicate that MDD is related to the disintegration of the left thalamus-right habenula tract function with an increased number of tracts as a compensational mechanism.

The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression

Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on individual people rather than a group. Therefore, machine learning has been increasingly conducted and applied to clinical practice. Several previous neuroimaging studies used machine learning for stratifying MDD patients from healthy controls as well as in differentially diagnosing MDD apart from other psychiatric disorders. Also, machine learning has been used to predict treatment response using magnetic resonance imaging (MRI) results. Despite the recent accomplishments of machine learning-based MRI studies, small sample sizes and the heterogeneity of the depression group limit the generalizability of a machine learning-based predictive model. Future neuroimaging studies should integrate various materials such as genetic, peripheral, and clinical phenotypes for more accurate predictability of diagnosis and treatment response.