Left Posterior Cingulate Cortex Research Articles

Context matters: task relevance shapes neural responses to emotional facial expressions

Recent research shows that emotional facial expressions impact behavioral responses only when their valence is relevant to the task. Under such conditions, threatening faces delay attentional disengagement, resulting in slower reaction times and increased omission errors compared to happy faces. To investigate the neural underpinnings of this phenomenon, we used functional magnetic resonance imaging to record the brain activity of 23 healthy participants while they completed two versions of the go/no-go task. In the emotion task (ET), participants responded to emotional expressions (fearful or happy faces) and refrained from responding to neutral faces. In the gender task (GT), the same images were displayed, but participants had to respond based on the posers’ gender. Our results confirmed previous behavioral findings and revealed a network of brain regions (including the angular gyrus, the ventral precuneus, the left posterior cingulate cortex, the right anterior superior frontal gyrus, and two face-responsive regions) displaying distinct activation patterns for the same facial emotional expressions in the ET compared to the GT. We propose that this network integrates internal representations of task rules with sensory characteristics of facial expressions to evaluate emotional stimuli and exert top-down control, guiding goal-directed actions according to the context.

Potential correlations between abnormal homogeneity of default mode network and personality or lipid level in major depressive disorder.

Default mode network (DMN) is one of the most recognized resting-state networks in major depressive disorder (MDD). However, the homogeneity of this network in MDD remains incompletely explored. Therefore, this study aims to determine whether there is abnormal network homogeneity (NH) of the DMN in MDD patients. At the same time, correlations between clinical variables and brain functional connectivity are examined. We enrolled 42 patients diagnosed with MDD and 42 HCs. A variety of clinical variables were collected, and data analysis was conducted using the NH and independent component analysis methods. The study shows that MDD patients have higher NH values in the left superior medial prefrontal cortex (MPFC) and left posterior cingulate cortex (PCC) compared to HCs. Additionally, there is a positive correlation between NH values of the left superior MPFC and Eysenck Personality Questionnaire values. NH values of the left PCC are positively linked to CHOL levels, LDL levels, and utilization scores. However, these correlations lose significance after the Bonferroni correction. Our findings indicate the presence of abnormal DMN homogeneity in MDD, underscoring the significance of DMN in the pathophysiology of MDD. Simultaneously, the study provides preliminary evidence for the correlation between clinical variables and brain functional connectivity.

Leptin’s Inverse Association With Brain Morphology and Depressive Symptoms: A Discovery and Confirmatory Study Across 2 Independent Samples

BackgroundMajor depressive disorder has a complex, bidirectional relationship with metabolic dysfunction, but the neural correlates of this association are not well understood. MethodsIn this cross-sectional investigation, we used a 2-step discovery and confirmatory strategy utilizing 2 independent samples (sample 1: 288 participants, sample 2: 196 participants) to examine the association between circulating indicators of metabolic health (leptin and adiponectin) and brain structures in individuals with major depressive disorder. ResultsWe found a replicable inverse correlation between leptin levels and cortical surface area within essential brain areas responsible for emotion regulation, such as the left posterior cingulate cortex, right pars orbitalis, right superior temporal gyrus, and right insula (standardized beta coefficient range: −0.27 to −0.49, puncorrected < .05). Notably, this relationship was independent of C-reactive protein levels. We also identified a significant interaction effect of leptin levels and diagnosis on the cortical surface area of the right superior temporal gyrus (standardized beta coefficient = 0.26 in sample 1, standardized beta coefficient = 0.30 in sample 2, puncorrected < .05). We also observed a positive correlation between leptin levels and atypical depressive symptoms in both major depressive disorder groups (r = 0.14 in sample 1, r = 0.29 in sample 2, puncorrected < .05). ConclusionsThe inverse association between leptin and cortical surface area in brain regions that are important for emotion processing and leptin’s association with atypical depressive symptoms support the hypothesis that metabolic processes may be related to emotion regulation. However, the molecular mechanisms through which leptin may exert these effects should be explored further.

Functional connectivity-hemodynamic (un)coupling changes in chronic mild brain injury are associated with mental health and neurocognitive indices: a resting state fMRI study.

To examine hemodynamic and functional connectivity alterations and their association with neurocognitive and mental health indices in patients with chronic mild traumatic brain injury (mTBI). Resting-state functional MRI (rs-fMRI) and neuropsychological assessment of 37 patients with chronic mTBI were performed. Intrinsic connectivity contrast (ICC) and time-shift analysis (TSA) of the rs-fMRI data allowed the assessment of regional hemodynamic and functional connectivity disturbances and their coupling (or uncoupling). Thirty-nine healthy age- and gender-matched participants were also examined. Patients with chronic mTBI displayed hypoconnectivity in bilateral hippocampi and parahippocampal gyri and increased connectivity in parietal areas (right angular gyrus and left superior parietal lobule (SPL)). Slower perfusion (hemodynamic lag) in the left anterior hippocampus was associated with higher self-reported symptoms of depression (r = - 0.53, p = .0006) and anxiety (r = - 0.484, p = .002), while faster perfusion (hemodynamic lead) in the left SPL was associated with lower semantic fluency (r = - 0.474, p = .002). Finally, functional coupling (high connectivity and hemodynamic lead) in the right anterior cingulate cortex (ACC)) was associated with lower performance on attention and visuomotor coordination (r = - 0.50, p = .001), while dysfunctional coupling (low connectivity and hemodynamic lag) in the left ventral posterior cingulate cortex (PCC) and right SPL was associated with lower scores on immediate passage memory (r = - 0.52, p = .001; r = - 0.53, p = .0006, respectively). Uncoupling in the right extrastriate visual cortex and posterior middle temporal gyrus was negatively associated with cognitive flexibility (r = - 0.50, p = .001). Hemodynamic and functional connectivity differences, indicating neurovascular (un)coupling, may be linked to mental health and neurocognitive indices in patients with chronic mTBI.

Cerebral Cortical Surface Structure and Neural Activation Pattern Among Adolescent Football Players

Recurring exposure to head impacts in American football has garnered public and scientific attention, yet neurobiological associations in adolescent football players remain unclear. To examine cortical structure and neurophysiological characteristics in adolescent football players. This cohort study included adolescent football players and control athletes (swimming, cross country, and tennis) from 5 high school athletic programs, who were matched with age, sex (male), and school. Neuroimaging assessments were conducted May to July of the 2021 and 2022 seasons. Data were analyzed from February to November 2023. Playing tackle football or noncontact sports. Structural magnetic resonance imaging (MRI) data were analyzed for cortical thickness, sulcal depth, and gyrification, and cortical surface-based resting state (RS)-functional MRI analyses examined the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and RS-functional connectivity (RS-FC). Two-hundred seventy-five male participants (205 football players; mean [SD] age, 15.8 [1.2] years; 5 Asian [2.4%], 8 Black or African American [3.9%], and 189 White [92.2%]; 70 control participants; mean [SD] age 15.8 [1.2] years, 4 Asian [5.7], 1 Black or African American [1.4%], and 64 White [91.5%]) were included in this study. Relative to the control group, the football group showed significant cortical thinning, especially in fronto-occipital regions (eg, right precentral gyrus: t = -2.24; P = .01; left superior frontal gyrus: -2.42; P = .002). Elevated cortical thickness in football players was observed in the anterior and posterior cingulate cortex (eg, left posterior cingulate cortex: t = 2.28; P = .01; right caudal anterior cingulate cortex 3.01; P = .001). The football group had greater and deeper sulcal depth than the control groups in the cingulate cortex, precuneus, and precentral gyrus (eg, right inferior parietal lobule: t = 2.20; P = .004; right caudal anterior cingulate cortex: 4.30; P < .001). Significantly lower ALFF was detected in the frontal lobe and cingulate cortex of the football group (t = -3.66 to -4.92; P < .01), whereas elevated ALFF was observed in the occipital regions (calcarine and lingual gyrus, t = 3.20; P < .01). Similar to ALFF, football players exhibited lower ReHo in the precentral gyrus and medial aspects of the brain, such as precuneus, insula, and cingulum, whereas elevated ReHo was clustered in the occipitotemporal regions (t = 3.17; P < .001; to 4.32; P < .01). There was no group difference in RS-FC measures. In this study of adolescent athletes, there was evidence of discernible structural and physiological differences in the brains of adolescent football players compared with their noncontact controls. Many of the affected brain regions were associated with mental health well-being.

The Neural Correlates of Individual Differences in Reinforcement Learning during Pain Avoidance and Reward Seeking.

Organisms learn to gain reward and avoid punishment through action-outcome associations. Reinforcement learning (RL) offers a critical framework to understand individual differences in this associative learning by assessing learning rate, action bias, pavlovian factor (i.e., the extent to which action values are influenced by stimulus values), and subjective impact of outcomes (i.e., motivation to seek reward and avoid punishment). Nevertheless, how these individual-level metrics are represented in the brain remains unclear. The current study leveraged fMRI in healthy humans and a probabilistic learning go/no-go task to characterize the neural correlates involved in learning to seek reward and avoid pain. Behaviorally, participants showed a higher learning rate during pain avoidance relative to reward seeking. Additionally, the subjective impact of outcomes was greater for reward trials and associated with lower response randomness. Our imaging findings showed that individual differences in learning rate and performance accuracy during avoidance learning were positively associated with activities of the dorsal anterior cingulate cortex, midcingulate cortex, and postcentral gyrus. In contrast, the pavlovian factor was represented in the precentral gyrus and superior frontal gyrus (SFG) during pain avoidance and reward seeking, respectively. Individual variation of the subjective impact of outcomes was positively predicted by activation of the left posterior cingulate cortex. Finally, action bias was represented by the supplementary motor area (SMA) and pre-SMA whereas the SFG played a role in restraining this action tendency. Together, these findings highlight for the first time the neural substrates of individual differences in the computational processes during RL.

Brain connectivity patterns associated with individual differences in the access to experience-near personal semantics: a resting-state fMRI study

It has been proposed that a continuum of specificity exists between episodic and semantic autobiographical memory. Personal semantics have been theorized to situate intermediately on this continuum, with more “experience-near” personal semantics (enPS) closer to the episodic end. We used individual differences in behavior as a model to investigate brain networks associated with the access to episodic autobiographical (EAM) and enPS information, assessing the relation between performance in the EAM and enPS conditions of the Autobiographical Fluency Task (AFT) and intrinsic brain connectivity. Results of an intrinsic connectivity contrast analysis showed that the global connectivity of two clusters in the left and right posterior cingulate cortex (PCC) was predicted by performance in the enPS conditions. Moreover, enPS scores predicted the connectivity strength of the right PCC with the bilateral anterior hippocampus (aHC), anterior middle temporal gyrus (aMTG) and medial orbitofrontal cortex, and the left aMTG and PCC. enPS scores also predicted the connectivity strength of the left PCC with the bilateral HC and MTG. The network highlighted involves parts of the core and of the dorsal medial subsystems of the Default Mode Network, in line with the proposal that enPS represents an intermediate entity between episodic and semantic memory.

Genetic evidence suggests a genetic association between major depressive disorder and reduced cortical gray matter volume: A Mendelian randomization study and mediation analysis

BackgroundSeveral studies have suggested an association between major depressive disorder (MDD) and abnormal brain structure. However, it is unclear whether MDD affects cortical gray matter volume, a common indicator of cognitive function. We aimed to determine whether MDD was associated with decreased cortical gray matter volume (GMV) through a Mendelian randomization (MR) study. MethodsWe obtained summary genetic data from a study conducted by the Psychiatric Genomics Consortium, which recruited a total of 480,359 participants (135,458 cases and 344,901 controls). Genetic tools—single nucleotide polymorphisms (SNPs)—of MDD were extracted from the study and their effects on gray matter volumes of the cortex and total brain were evaluated in a large cohort from the UK Biobank (n = 8427). The effects of the SNPs were pooled using inverse variance weighted (IVW) analysis and further tested in several sensitivity analyses. We tested whether C-reactive protein (CRP) levels and interleukin-6 signaling were the mediators of the effects using a multivariate MR model. ResultsThirty-three SNPs were identified and adopted as genetic tools for predicting MDD. IVW analysis showed that MDD was associated with lower overall GMV (beta value −0.106, 95%CI -0.188 to −0.023, p = 0.011) in the frontal pole (left frontal pole, −0.152, 95%CI -0.177 to −0.127, p = 0.013; right frontal pole, −0.133, 95%CI -0.253 to −0.013, p = 0.028). Multivariate and mediation analysis showed that interleukin-6 was an important mediator of GMV reduction. Reverse causality analysis found no evidence that total GMV affected the risk of MDD, but showed that increased left precuneus cortex volume and left posterior cingulate cortex volume were associated with increased risk of MDD. LimitationsPotential pleiotropic effects and overestimation of real-world effects. Key assumptions for MR analysis may not be satisfactorily met. ConclusionMDD was associated with a reduced GMV, and interleukin-6 might be a mediator of GMV reduction.

Functional Connectivity, Physical Activity, and Behavioral Abnormality in Patients with Vascular Cognitive Impairment

Purpose of the research: Neuropsychiatric Symptoms (NPSs) can negatively impact the survival and quality of life in Vascular Cognitive Impairment (VCI) patients. Physical Activity (PA) has been shown to reduce NPSs in dementia patients, possibly by influencing synaptic plasticity. This study investigates the relationship between NPSs, Default Mode Network (DMN), and PA in patients with VCI. Methods: The study included 42 VCI patients. Functional Connectivity (FC) within the DMN and neurobehavioral performance was assessed. NPSs were categorized. The severity of Hyperactivity and Behavioral Symptoms (HBS) was quantified using a Hyperactivity and Behavioral Composite Score (HBCS). Patients’ PA levels were measured using Fitbit Charge 2. Principal results: After accounting for disease severity, increased FC between the left Posterior Cingulate Cortex (PCC) seed and the right inferior parietal gyrus was linked to more severe HBS. There was an inverse correlation between HBCS and average step counts per day (steps/d) as well as average distance per day (km/d). This suggests that higher levels of PA were associated with less severe HBS. HBCS was also inversely correlated with steps/d and km/d, reinforcing the idea that increased PA was linked to reduced symptom severity. Major conclusions: The study concludes that increased FC within the DMN is associated with more severe HBS in VCI patients. Greater levels of PA (measured by step counts and distance) were associated with a reduction in the severity of HBS. This suggests that FC within the DMN may play a role in the modulation of HBS by PA in VCI patients.

Abnormal alterations of regional spontaneous neuronal activity and functional connectivity in insomnia patients with difficulty falling asleep: a resting-state fMRI study

BackgroundInsomnia disorder (ID) seriously affects people’s daily life. Difficulty falling asleep is the most commonly reported complaint in patients with ID. However, the mechanism of prolonged sleep latency (SL) is still obscure. The aim of our present study was to investigate the relationship between prolonged SL and alterations in spontaneous neural activity and brain functional connectivity (FC) in ID patients using functional magnetic resonance imaging (fMRI).MethodsA total of 52 insomniacs with difficulty falling asleep and 30 matched healthy controls (HCs) underwent resting-state fMRI. The amplitude of low-frequency fluctuation (ALFF) was measured and group differences were compared. The peak areas with significantly different ALFF values were identified as the seed regions to calculate FC to the whole brain. SL was assessed by a wrist actigraphy device in ID patients. The Pittsburgh Sleep Quality Index (PSQI), Hamilton Anxiety Rating Scale (HAMA), and Hyperarousal Scale (HAS) were evaluated in both ID patients and HCs. Finally, correlation analyses were performed between the clinical features and FC/ALFF values.ResultsID patients showed higher PSQI, HAMA, HAS scores than HCs. The functional MRI results indicated increased ALFF value in the left insula and right amygdala and decreased ALFF value in the right superior parietal lobe (SPL) in ID patients. The seed-based FC analysis demonstrated increased FC between the left insula and the bilateral precentral gyrus and FC between the right amygdala and the left posterior cingulate cortex (PCC) in patients with ID. Correlation analysis indicated that the increased FC value of the right amygdala-left PCC was positively correlated with SL measured by actigraphy.ConclusionThis study revealed abnormal regional spontaneous fluctuations in the right amygdala, left insula, and right SPL, as well as increased FC in the left insula-precentral and right amygdala-left PCC. Moreover, the prolonged SL was positively correlated with the abnormal FC in the right amygdala-left PCC in ID patients. The current study showed the correlation between prolonged SL and the abnormal function of emotion-related brain regions in ID patients, which may contribute to a better understanding of the neural mechanisms underlying difficulty falling asleep in patients with ID.Clinical Trial Registrationhttp://www.chictr.org.cn., ChiCTR1800015282. Registered on 20th March 2018.

Within‐network changes in strength of the default mode network relate to Alzheimer’s Disease biomarkers in APOE e4 carriers

AbstractBackgroundResting state functional connectivity measured with MRI (rsfMRI) is disrupted in Alzheimer’s Disease (AD), although reports of rsfMRI changes in adults with the apolipoprotein E (APOE) ε4 genotype (e4+) have been inconsistent. Here, we compare default mode network (DMN) connectivity between e4+ and non‐carriers (e4‐) groups and assess the relationship to AD‐related biomarkers.MethodSixty‐five e4+ (mean age 70.2 ± 4.1; 33 males) and 60 e4‐ (mean age 71.9 ± 4.1; 28 males) subjects underwent a whole‐brain anatomical MPRAGE and a rsfMRI scan. To focus on the DMN, ROIs were placed in the bilateral posterior cingulate cortex (PCC). The most highly correlated voxels between the left and right PCC ROIs were used to calculate left and right PCC rsfMRI z‐maps. Group differences were assessed and mean connectivity in each significant region was correlated with Aβ PET centiloid values and cerebrospinal fluid (CSF) Aβ42/ Aβ40 ratio, phosphorylated‐tau (p‐tau 181), and total‐tau (t‐tau).ResultEight regions showed group differences (p &lt; 0.05; Figure 1). In the e4+ group, right PCC connectivity to the bilateral anterior middle temporal gyri (Figure 1: ROIs 1 and 2) was negatively related to both centiloid score (p &lt; 0.03) and to CSF p‐tau (p &lt; 0.008; Figure 2). Left PCC connectivity to the right anterior middle temporal gyrus was negatively related to centiloid score (r = ‐0.26, p &lt; 0.042) and CSF p‐tau (r = ‐0.31, p &lt; 0.024). Biomarker values in the e4‐ group were not significantly related to connectivity.ConclusionOur findings are consistent with reports of weakened DMN connectivity in e4+ adults and suggest that differences in connectivity strength may be mediated by amyloid or tau deposition.This work was supported by the National Institutes of Health (R01AG022304).

Comparing feature selection methods to improve the classification of Mild Cognitive Impairment patients based on Magnetoencephalography data

AbstractBackgroundMachine learning (ML) algorithms are generating great interest for early diagnose of Alzheimer’s disease (AD). Despite several approaches have been developed, limited research has been conducted on the impact of feature selection in classification performance. In this study we compare two feature selection methods, LASSO and Elastic Nets (EN), applied to ML algorithms based on magnetoencephalography (MEG) data from mild cognitive impairment (MCI) patients.MethodAn eyes‐closed resting‐state MEG recording was performed by 262 participants (117 healthy controls and 145 MCI). Functional connectivity measures were estimated for all classical frequency bands using the Phase‐Locking Value. To develop the ML algorithms, specific regions of interest from the default mode network, based on the Automatic Anatomical Labelling atlas, were selected, resulting in a total of 325 features. After dividing the dataset into training (80%) and test (20%) sets, feature selection with 10000 iterations and five‐fold cross validation was performed independently for each band using LASSO and EN algorithms. Afterwards, several classification models were trained with a five‐fold cross validation and then validated on the test set, over 500 iterations.ResultThe classifier showing the best performance was the linear Support Vector Machine for the beta frequency band (12‐30 Hz). Given the similarity of the training and test performance values, the results reveal that overfitting was avoided when using the 45 features selected by LASSO instead of the 47 features selected by EN, reaching a validation accuracy of 77%. Additionally, LASSO consistently identified three functional connectivity links as relevant in all frequency bands but alpha: right superior frontal gyrus &amp; right superior frontal gyrus orbital, left posterior cingulate cortex &amp; left hippocampus, right posterior cingulate cortex &amp; right precuneus.ConclusionML algorithms applied to MEG data can successfully discriminate MCI patients from healthy controls. In addition, LASSO algorithm applied for feature selection leads to better classification performance. Moreover, the resulting relevant functional connectivity links involve areas affected by AD pathology even in early stages of its development (López‐Sanz et al., 2019). Further exploration of this methodology is needed, combining this data with structural neuroimaging or complementary biomarkers.

The role of transcranial direct current stimulation in improving learning and memory performances of patients with mild cognitive impairment and mild Alzheimer’s disease: a functional magnetic resonance imaging study

AbstractBackgroundTranscranial direct current stimulation (tDCS) is a safe non‐invasive brain stimulation (NIBS) procedure which helps to stimulate a particular region of interest by modulating the neuronal firing rate, thereby modulating the cognitive functioning. The tDCS intervention is known to show improvement on memory recall and recognition in patients with mild cognitive impairment (MCI) and mild Alzheimer’s Disease (mild AD)MethodWe investigated the effect of anodal stimulation at the left dorsolateral prefrontal cortex (DLPFC) and cathodal stimulation at the right supra‐orbital area in patients with MCI (n = 18) and mild AD (n = 20) (Refer Table 1), after due approval from Institute Ethics Committee NIMHANS. Both baseline and post‐intervention assessments for a single patient were administered by the different assessors allocated randomly using NIMHANS neuropsychological battery for elderly (NNB‐E). Two resting state functional magnetic resonance imaging (rsfMRI) acquisitions, before and after the ten sessions of tDCS, each session lasting 20 minutes. A spherical mask of 5 mm diameter was chosen a priori on the left middle frontal gyrus (LMFG) as a seed for performing seed based functional connectivity (sbFC) analysis using default preprocessing pipeline and pre‐post analysis setup in CONN 18b toolbox.ResultThe combined sample of patients with MCI and mild AD showed significant improvement in word list learning trial 3 (pre tDCS 5.85 (1.59); post tDCS 6.63 (1.67); Z = 3.586, p&lt;0.0001) and delayed recall (pre tDCS 1.50 (1.88); post tDCS 2.45 (2.33); Z = 3.656, p&lt;0.0001) scores using NNB‐E. The sbFC analysis showed significantly reduced functional connectivity between left MFG (the region of anodal stimulation) and posterior cingulate cortex (PCC) as well as precuneus cortex (cluster‐level extent threshold p‐FDR &lt;0.05) after tDCS. Post‐hoc tests showed that these reductions after tDCS intervention were also found to be significantly associated with attention and auditory verbal learning memory scores (Refer Figure 1 and Table 2).ConclusionThe tDCS intervention helped reducing the functional connectivity at rest, which was responsible for compensatory neuronal processes found in early AD. These findings unravel the therapeutic role of tDCS by enhancing auditory verbal memory performance and altering the functional connectivity at PCC and precuneus cortex in earlier stages of AD.

Progressive Cerebrovascular Reactivity Reduction Occurs in Parkinson's Disease: A Longitudinal Study.

The change of microvascular function over the course of Parkinson's disease (PD) remains unclear. We aimed to ascertain regional cerebrovascular reactivity (CVR) changes in the patients with PD at baseline (V0) and during a 2-year follow-up period (V1). We further investigated whether alterations in CVR were linked to cognitive decline and brain functional connectivity (FC). We recruited 90 PD patients and 51 matched healthy controls (HCs). PD patients underwent clinical evaluations, neuropsychological assessments, and magnetic resonance (MR) scanning at V0 and V1, whereas HCs completed neuropsychological assessments and MR at baseline. The analysis included evaluating CVR and FC maps derived from resting-state functional magnetic resonance imaging and investigating CVR measurement reproducibility. Compared with HCs, CVR reduction in left inferior occipital gyrus and right superior temporal cortex at V0 persisted at V1, with larger clusters. Longitudinal reduction in CVR of the left posterior cingulate cortex correlated with decline in Trail Making Test B performance within PD patients. Reproducibility validation further confirmed these findings. In addition, the results also showed that there was a tendency for FC to be weakened from posterior to anterior with the progression of the disease. Microvascular dysfunction might be involved in disease progression, subsequently weaken brain FC, and partly contribute to executive function deficits in early PD. © 2023 International Parkinson and Movement Disorder Society.

Gray matter structural covariance networks patterns associated with autopsy-confirmed LATE-NC compared to Alzheimer's disease pathology

BackgroundCases with the limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy neuropathologic change (LATE-NC), Alzheimer's disease (AD), and mixed AD+TDP-43 pathology (AD+LATE-NC) share similar symptoms, which makes it a challenge for accurate diagnosis. Exploring the patterns of gray matter structural covariance networks (SCNs) in these three types may help to clarify the underlying mechanism and provide a basis for clinical interventions. MethodsWe included ante-mortem MRI data of 10 LATE-NC, 39 CE, and 25 CE+LATE-NC from the ADNI autopsy sample. We used four regions of interest (left posterior cingulate cortex, right entorhinal cortex, frontoinsular and dorsolateral prefrontal cortex) to anchor the default mode network (DMN), salience network (SN), and executive control network (ECN). Finally, we assessed the SCN alternations using a multi-regression model-based linear-interaction analysis. ResultsCases with autopsy-confirmed LATE-NC and AD showed increased structural associations involving DMN, ECN, and SN. Cases with AD+LATE-NC showed increased structural association within DMN while decreased structural association between DMN and ECN. The volume of peak clusters showed significant associations with cognition and AD pathology. ConclusionsThis study showed different SCN patterns in the cases with LATE-NC, AD, and AD+LATE-NC, and indicated the network disconnection mechanism underlying these three neuropathological progressions. Further, SCN may serve as an effective biomarker to distinguish between different types of dementia.

Rumination-Focused Cognitive Behavioral Therapy Reduces Rumination and Targeted Cross-network Connectivity in Youth With a History of Depression: Replication in a Preregistered Randomized Clinical Trial

BackgroundRumination-focused cognitive behavioral therapy (RF-CBT) is designed to reduce depressive rumination or the habitual tendency to dwell on experiences in a repetitive, negative, passive, and global manner. RF-CBT uses functional analysis, experiential exercises, and repeated practice to identify and change the ruminative habit. This preregistered randomized clinical trial (NCT03859297, R61) is a preregistered replication of initial work. We hypothesized a concurrent reduction of both self-reported rumination and cross-network connectivity between the left posterior cingulate cortex and right inferior frontal and inferior temporal gyri. MethodsSeventy-six youths with a history of depression and elevated rumination were randomized to 10 to 14 sessions of RF-CBT (n = 39; 34 completers) or treatment as usual (n = 37; 28 completers). Intent-to-treat analyses assessed pre-post change in rumination response scale and in functional connectivity assessed using two 5 minute, 12 second runs of resting-state functional magnetic resonance imaging. ResultsWe replicated previous findings: a significant reduction in rumination response scale and a reduction in left posterior cingulate cortex to right inferior frontal gyrus/inferior temporal gyrus connectivity in participants who received RF-CBT compared with those who received treatment as usual. Reductions were large (z change = 0.84; 0.73, respectively [ps < .05]). ConclusionsThis adolescent clinical trial further demonstrates that depressive rumination is a brain-based mechanism that is modifiable via RF-CBT. Here, we replicated that RF-CBT reduces cross-network connectivity, a possible mechanism by which rumination becomes less frequent, intense, and automatic. This National Institute of Mental Health-funded fast-fail study continues to the R33 phase during which treatment-specific effects of RF-CBT will be compared with relaxation therapy.

48 Should I Stay or Should I Go? Neural Circuits Underlying Decisions to Explore or Exploit

Objective:Adaptive decision-making is necessary to sustain functional independence. Maladaptive decisions are among the most prevalent features of psychological and neurological disorders. One crucial aspect of decision-making involves arbitrating between exploring new avenues with risky but potentially lucrative outcomes or exploiting prior knowledge and endorsing predictable outcomes. Balancing this dichotomy creates a behavioral tension that shapes all decisions and is termed the exploration-exploitation trade-off. This trade-off has been linked to reward and affective drives and associated neural circuitry as well as neuropsychological dysfunction. However, the neural mechanisms underlying the exploration-exploitation trade-off are still uncertain, due to the scarcity of literature and the heterogeneity of paradigms. This study aimed to systematically quantify and disambiguate neuroanatomical correlates of the exploration-exploitation tradeoff in a normative adult sample. These findings provide a necessary starting point for future investigations of this fundamental aspect of decision-making across clinical populations, with potential implications for assessment and intervention.Participants and Methods:We used the effect-location method of meta-analysis to analyze data from 10 functional neuroimaging studies investigating the exploration-exploitation tradeoff in non-clinical samples. We analyzed the location and frequency of significant neural activations across studies for both explorative and exploitative decisions and characterized them as core and non-core regions. Core activations were defined as those reported in over 50% of studies. Secondary and tertiary activations were defined as those reported in 40% and 30% of studies, respectively. The present review was conducted in accordance with the guidelines of the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.Results:The results revealed that explorative and exploitative choice behaviours differed markedly with respect to associated patterns of task-related brain activity. Exploration was associated with activity in brain regions implicated in externally directed, goal-based attentional processing and reward-related uncertainty, mainly tapping bilateral parietal and frontal circuitry, with relatively high consistency across studies. A core explorative network was revealed, consisting of activity in the frontal polar cortex, the dorsal anterior cingulate cortex, the bilateral medial frontal gyrus, the bilateral precuneus, and the bilateral intraparietal sulcus. Secondary and tertiary regions were also detected, including the bilateral anterior insula, the left precentral gyrus, the bilateral superior frontal gyrus, the right inferior frontal gyrus, the left supplementary motor area, the bilateral superior parietal lobule, and the bilateral thalamus. Exploitation was associated with brain regions implicated in internally directed processes including reward valuation, motivation, and memory. Core exploitative activations included the ventromedial prefrontal cortex, the bilateral anterior cingulate cortex, and the bilateral orbitofrontal cortex. Secondary and tertiary activations included the bilateral hippocampus, the left middle temporal gyrus, the bilateral angular gyrus, the left posterior cingulate cortex, the left superior frontal gyrus, and the bilateral superior temporal gyrus.Conclusions:The exploration-exploitation trade-off provides a novel paradigmatic approach to study adaptive and maladaptive decision-making behaviour in humans. Our findings support the neural dichotomization of exploration and exploitation and illuminate potential neural networks underlying this fundamental feature of decision-making. Understanding these mechanistic networks opens a new avenue of inquiry into decision-making deficits in clinical populations, including neurodegenerative, neurodevelopmental, and neuropsychiatric syndromes.

Correlation between low frequency fluctuation and cognitive performance in bipolar disorder patients with suicidal ideation

BackgroundPatients with bipolar disorder (BD) are at high risk of suicidal ideation (SI), and BD patients with suicidal ideation (BDSI) have shown marked abnormalities in spontaneous brain function. Cognitive impairment, on the other hand, is considered to be one of the core symptoms of BD. However, few studies have addressed the association between cognitive performance and abnormal spontaneous brain function in BDSI. MethodsIn the current study, the MATRICS Consensus Cognitive Battery (MCCB) was used to assess cognitive performance in BDSI (n = 20), BD subjects without suicidal ideation (BDNSI) (n = 24) and healthy controls (HC) (n = 30). Their cognitive performance was then correlated with amplitude of low frequency fluctuation (ALFF) values obtained by resting-state functional magnetic resonance imaging (rs-fMRI). ResultsWe found that ALFF was significantly higher in the left precuneus and right posterior cingulate cortex in the BDSI group and significantly lower in the right precuneus in the BDNSI group than in the HC group. In addition, in the BDSI group, visual learning performance was positively correlated with ALFF values in the left precuneus. ConclusionsOur findings support the notion that BD patients present with ALFF abnormalities, which are associated with cognitive performance in BDSI.

Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia

Objective. Schizophrenia (SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals. Approach. For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature. Main results. When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ. Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.

Brain-gut axis mechanism of subthreshold nonsuicidal self-injury addictive features in adolescents.

Nonsuicidal self-injury (NSSI) is associated with an increased risk of suicide. As the diagnostic criteria outlined in DSM-5 and other related clinical studies, a patient must have engaged in self-injurious behavior at least 5 times within the past year. However, patients with fewer than 5 self-injury behaviors should not be ignored. Our study included 46 adolescents aged 10-19 years with subthreshold NSSI (sNSSI), along with a control group of 50 healthy adolescents matched for age and other factors. We collected resting-state functional magnetic resonance imaging data and stool samples. The Ottawa Self-Injury Inventory and Deliberate Self-Harm Inventory were used to evaluate self-harm behaviors and addictive features. Local brain activity was assessed using fractional amplitude of low-frequency fluctuations (fALFF), and brain regions with abnormal fALFF were selected as seeds for whole-brain functional connectivity analysis. Stool samples were identified using 16S rDNA amplicon sequencing, and the LDA Effect Size method was used to explore significant differences between grouped samples. Mediation analysis was performed to investigate the brain-gut axis mechanisms of addictive features in sNSSI. We found that compared with healthy controls, sNSSI patients have abnormal fALFF in left thalamus and posterior cingulate cortex, dysconnectivities of left thalamus, and decreased Prevotellaceae. Our results suggested that addictive features of sNSSI may have a brain-gut mechanism. Furtherly, patients with 1-4 NSSI behaviors in the past year should have separate name for identification, such as "subthreshold NSSI".