BACKGROUND:Obesity, which is common in bipolar disorder (BD), is associated with smaller hippocampal volumes. We do not know the role of weight/weight gain in relation to longitudinal hippocampal changes among individuals with BD. METHODS:In collaboration with the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-BD Working Group, we obtained T1-weighted magnetic resonance imaging and clinical data from 233 participants with BD and 701 healthy control participants (HCs) scanned twice, 2.84 ± 1.63 years apart on average. We estimated subcortical volumes using FreeSurfer longitudinal image processing stream and used linear mixed models to assess the bidirectional relationship between baseline body mass index (BMI) or BMI change and hippocampal volume or volume change. While the hippocampus was our a priori region of interest, we repeated these analyses in other subcortical regions. RESULTS:Baseline BMI predicted future hippocampal atrophy, but baseline brain structure did not predict future weight changes. BMI increased significantly over time (F1,1085 = 15.98, p < .001). Individuals with lower baseline BMI experienced greater weight gain (F1,922 = 105.12, p < .001). Greater weight gain was associated with greater hippocampal atrophy over time (F1,899 = 16.33, p = .001), more so in participants with BD than HCs (F1,898 = 6.91, p = .009). Consequently, lower baseline BMI predicted greater future hippocampal volume loss (F1,904 = 14.77, p < .001). These associations were not observed in other subcortical regions. CONCLUSIONS:Our findings suggest that weight gain is a modifiable risk factor for hippocampal atrophy, especially in individuals with lower BMI and those with BD. Prevention of weight gain in general, but especially in people with BD, could provide neuroprotective benefits.

Major Depressive Disorder (MDD) is a highly prevalent, severe mental health condition that constitutes one of the leading causes of disability worldwide. While recent animal studies suggest a causal role of the gut microbiome in the pathophysiology of MDD models, evidence in humans is still unclear due to small sample sizes, inconsistent clinical assessment of MDD diagnosis, and methodological limitations regarding causal inference in cross-sectional data. Here, we explicitly address these shortcomings to investigate the potential causal link between the gut microbiome and MDD: First, we replicate previously reported microbiome-depression associations using one of the largest multicenter MDD cohorts for which microbiome data and in-depth diagnostic assessment are available (N = 1,269 MDD patients and controls). We find a significant difference between healthy controls and MDD patients for the relative abundance of four taxa: Eggerthella, Hungatella, Coprobacillus, and Lachnospiraceae FCS020. Second, we employ state-of-the-art, fully data-driven causal inference tools within Judea Pearl's framework, allowing us to derive model constraints from the data rather than relying on potentially strong, unrealistic assumptions. Using this approach, we found evidence for Eggerthella and Hungatella as potential causal contributors to MDD. Furthermore, we show that the potential causal effects of Eggerthella and Hungatella on MDD persist beyond the influence of body mass index, revealing two distinct potential causal pathways linking the gut microbiome to MDD. Finally, the difference in relative abundance of these taxa between healthy and MDD patients was independent of antidepressant medication. Our study provides the first data-driven evidence for a potential causal role of gut microbiota in the pathophysiology of depression in humans.

BACKGROUND:The gut microbiome has been linked to major depressive disorder (MDD), yet it remains unclear whether antidepressant treatment influences these associations. This study aimed to clarify the role of serotonin reuptake inhibitors (SSRI/SNRI) in shaping gut microbiome changes observed in MDD. METHODS:We conducted cross-sectional analyses in two independent patient cohorts (total N = 1802) and a meta-analysis across both cohorts, comparing the gut microbiome of MDD patients with and without SSRI/SNRI treatment. RESULTS:Here we show that SSRI/SNRI treatment is consistently associated with reduced Clostridium sensu stricto 1 abundance. This effect is specific to SSRI/SNRI treatment and not observed with other psychotropic medications. Importantly, reductions in Clostridium sensu stricto 1 in MDD compared to unaffected controls are explained by SSRI/SNRI medication status. CONCLUSIONS:Antidepressant treatment is an important factor shaping gut microbiome alterations linked to MDD, underscoring the need to account for medication effects and potentially informing future microbiome-based strategies to improve treatment response.

The structural neuroanatomy underlying Major Depressive Disorder (MDD) remains elusive and even large-scale neuroimaging studies and meta-analyses produced inconsistent outcomes with marginal effect sizes. Furthermore, the replicability of brain-psychopathology associations is under scrutiny, attributable to inadequately powered sample sizes and inflated effect sizes. The present study aimed to examine the effect sizes, replicability, and generalizability of brain structural alterations in MDD, harnessing the wealth of three large-scale clinical cohorts. We used three independent cohorts totaling n = 4021 deeply characterized participants (aged 15-65 years), encompassing MDD patients (n = 1764), and healthy controls (HC; n = 2257) with individual MRI data. The diagnosis was confirmed by structured clinical interviews. Brain-wide case-control differences in voxel-based morphometry were tested. We conducted pooled analyses to maximize statistical power, and further investigated cross-cohort replicability and generalizability by inspecting 1) correlations between t-maps from each cohort, 2) converging significance across the three cohorts, and 3) by using a cross-validation framework, iterating through cohorts as independent test sets. Pooling all individuals together yielded reduced gray matter volumes in patients with MDD in widespread bilateral clusters, covering the insula, thalamus, orbitofrontal cortex, and a parahippocampal-fusiform-lingual complex. No evidence was found for differences within the hippocampus. The largest effect size was found in the bilateral anterior insula (partial R2 = 0.01). Analyzing cohorts separately yielded 1) t-map correlations between cohorts (r = 0.212 to r = 0.281), 2) replicability indicated by overlapping significance in multiple areas exceeding thresholds in each single cohort, and 3) generalizability of case-control differences in clusters that were deemed non-significant in single cohorts, implicating further frontal, temporal and cerebellar regions. Results indicate that gray matter correlates of MDD are subtle but nevertheless replicable and generalizable. These alterations are localized in an array of regions involved in emotion regulation and sensory processing. Our investigation underscores the need to investigate the replicability and generalizability of mental health neuroimaging findings and provides a tangible framework for this endeavor.

BACKGROUND:Lysophosphatidic acid (LPA) is a bioactive phospholipid that affects hippocampal excitatory synaptic transmission. RESULTS:Here we provide in vitro evidence that LPA elicits intracellular calcium concentration ([Ca2+]i) transients by LPA2 receptor activation in primary cultured hippocampal mouse neurons. Downstream and via Gi-coupling, this led to phospholipase C (PLC) activation, inositol (1,4,5) trisphosphate (IP3)-induced Ca2+ release (IICR) and voltage gated Ca2+ channel activation. In addition, we found that LPA elevated [Ca2+]i, not only in the soma but also in presynaptic terminals. This altered the frequency of spontaneous vesicle release specifically in excitatory synapses. However, against our expectations, LPA reduced the frequency of miniature excitatory postsynaptic currents. This was due to a depletion of releasable vesicles resulting from a slowed recycling. SynaptopHluorin based measurements indicated a transient augmentation of release followed by prolonged persistence of vesicles at the membrane. Concordant to our previous findings on ex vivo brain slices, LPA increased spontaneous glutamatergic vesicle release in Banker style astrocytic co-cultures. Our results indicate that pro-excitatory LPA effects critically depend on stable vesicle pools. CONCLUSIONS:Taken together, our data further support membrane derived phospholipids as active modulators of excitatory synaptic transmission.

Importance:Soft drink consumption is linked to negative physical and mental health outcomes, but its association with major depressive disorder (MDD) and the underlying mechanisms remains unclear. Objective:To examine the association between soft drink consumption and MDD diagnosis and severity and whether this association is mediated by changes in the gut microbiota, particularly Eggerthella and Hungatella abundance. Design, Setting, and Participants:This multicenter cohort study was conducted in Germany using cross-sectional data from the Marburg-Münster Affective Cohort. Patients with MDD and healthy controls (aged 18-65 years) recruited from the general population and primary care between September 2014 and September 2018 were analyzed. Data analyses were conducted between May and December 2024. Main Outcomes and Measures:Primary analyses included multivariable regression and analysis of variance (ANOVA) models examining the association between soft drink consumption and MDD diagnosis and symptom severity, controlling for site and education, and Eggerthella and Hungatella abundance, controlling for site, education, and library size. Mediation analyses tested whether microbiota abundance mediated the soft drink-MDD link. Results:A total of 405 patients with MDD (275 female patients [67.9%]; mean [SD] age, 36.37 [13.33] years) and 527 healthy controls (345 female controls [65.5%]; mean [SD] age, 35.33 [13.13] years) were included. Soft drink consumption predicted MDD diagnosis (odds ratio [OR], 1.081; 95% CI, 1.008-1.159; P = .03) and symptom severity (P < .001; partial η2 [ηp2], 0.012; 95% CI, 0.004-0.035), with stronger effects in women (diagnosis: OR, 1.167; 95% CI, 1.054-1.292; P = .003; severity: P < .001; ηp2, 0.036; 95% CI, 0.011-0.062). In women, consumption was linked to increased Eggerthella (P = .007; ηp2, 0.017; 95% CI, 0.0002-0.068), but not Hungatella abundance. Mediation analyses confirmed that Eggerthella significantly mediated the soft drink-MDD association (diagnosis: P = .011; severity: P = .005), explaining 3.82% and 5.00% of the effect, respectively. Conclusions and Relevance:In this cohort study, it was found that soft drink consumption may contribute to MDD through gut microbiota alterations, notably involving Eggerthella. Public health strategies to reduce soft drink intake may help mitigate depression risk, especially among vulnerable populations; in addition, interventions for depression targeting the microbiome composition appear promising.

Background Cognitive deficits are a key source of disability in individuals with major depressive disorder (MDD) and worsen with disease progression. Despite their clinical relevance, the underlying mechanisms of cognitive deficits remain poorly elucidated, hampering effective treatment strategies. Emerging evidence suggests that alterations in white matter microstructure might contribute to cognitive dysfunction in MDD. We aimed to investigate the complex association between changes in white matter integrity, cognitive decline, and disease course in MDD in a comprehensive longitudinal dataset. Methods In the naturalistic, observational, prospective, case-control Marburg-M & uuml;nster Affective Disorders Cohort Study, individuals aged 18-65 years and of Caucasian ancestry were recruited from local psychiatric hospitals in M & uuml;nster and Marburg, Germany, and newspaper advertisements. Individuals diagnosed with MDD and individuals without any history of psychiatric disorder (ie, healthy controls) were included in this subsample analysis. Participants had diffusion-weighted imaging, a battery of neuropsychological tests, and detailed clinical data collected at baseline and at 2 years of follow-up. We used linear mixed-effect models to compare changes in cognitive performance and white matter integrity between participants with MDD and healthy controls. Diffusion-weighted imaging analyses were conducted using tract-based spatial statistics. To correct for multiple comparisons, threshold free cluster enhancement (TFCE) was used to correct alpha-values at the family-wise error rate (FWE; p tfce-FWE ). Effect sizes were estimated by conditional, partial R2 2 values (sr2) sr 2 ) following the Nakagawa and Schielzeth method to quantify explained variance. The association between changes in cognitive performance and changes in white matter integrity was analysed. Finally, we examined whether the depressive disease course between assessments predicted cognitive performance at follow-up and whether white matter integrity mediated this association. People with lived experience were not involved in the research and writing process. Findings 881 participants were selected for our study, of whom 418 (47%) had MDD (mean age 368 years [SD 134], 274 [66%] were female, and 144 [34%] were male) and 463 (53%) were healthy controls (mean age 356 years [135], 295 [64%] were female, and 168 [36%] were male). Baseline assessments were done between Sept 11, 2014, and June 3, 2019, and after a mean follow-up of 220 years (SD 019), follow-up assessments were done between Oct 6, 2016, and May 31, 2021. Participants with MDD had lower cognitive performance than did healthy controls (p<00001, sr 2 =0056), regardless of timepoint. Analyses of diffusion-weighted imaging indicated a significant diagnosis x time interaction with a steeper decline in white matter integrity of the superior longitudinal fasciculus over time in participants with MDD than in healthy controls (ptfce-FWE=0026, tfce-FWE =0026, sr 2 =0002). Furthermore, cognitive decline was robustly associated with the decline in white matter integrity over time across both groups (ptfce-FWE<00001, tfce-FWE < 0 0001 , sr 2 =0004). In participants with MDD, changes in white matter integrity (p=00040, f3=0071) and adverse depressive disease course (p=00022, f3=-0073) independently predicted lower cognitive performance at follow-up. Interpretation Alterations of white matter integrity occurred over time to a greater extent in participants with MDD than in healthy controls, and decline in white matter integrity was associated with a decline in cognitive performance across groups. Our findings emphasise the crucial role of white matter microstructure and disease progression in depression-related cognitive dysfunction, making both priority targets for future treatment development. Funding German Research Foundation (DFG). Copyright (c) 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license.

Lysophosphatidic acids (LPAs) evoke nociception and itch in mice and humans. In this study, we assessed the signaling paths. Hydroxychloroquine was injected intradermally to evoke itch in mice, which evoked an increase of LPAs in the skin and in the thalamus, suggesting that peripheral and central LPA receptors (LPARs) were involved in HCQ-evoked pruriception. To unravel the signaling paths, we assessed the localization of candidate genes and itching behavior in knockout models addressing LPAR5, LPAR2, autotaxin/ENPP2 and the lysophospholipid phosphatases, as well as the plasticity-related genes Prg1/LPPR4 and Prg2/LPPR3. LacZ reporter studies and RNAscope revealed LPAR5 in neurons of the dorsal root ganglia (DRGs) and in skin keratinocytes, LPAR2 in cortical and thalamic neurons, and Prg1 in neuronal structures of the dorsal horn, thalamus and SSC. HCQ-evoked scratching behavior was reduced in sensory neuron-specific Advillin-LPAR5−/− mice (peripheral) but increased in LPAR2−/− and Prg1−/− mice (central), and it was not affected by deficiency of glial autotaxin (GFAP-ENPP2−/−) or Prg2 (PRG2−/−). Heat and mechanical nociception were not affected by any of the genotypes. The behavior suggested that HCQ-mediated itch involves the activation of peripheral LPAR5, which was supported by reduced itch upon treatment with an LPAR5 antagonist and autotaxin inhibitor. Further, HCQ-evoked calcium fluxes were reduced in primary sensory neurons of Advillin-LPAR5−/− mice. The results suggest that LPA-mediated itch is primarily mediated via peripheral LPAR5, suggesting that a topical LPAR5 blocker might suppress “non-histaminergic” itch.

Excitation/inhibition (E/I) balance plays important roles in mental disorders. Bioactive phospholipids like lysophosphatidic acid (LPA) are synthesized by the enzyme autotaxin (ATX) at cortical synapses and modulate glutamatergic transmission, and eventually alter E/I balance of cortical networks. Here, we analyzed functional consequences of altered E/I balance in 25 human subjects induced by genetic disruption of the synaptic lipid signaling modifier PRG-1, which were compared to 25 age and sex matched control subjects. Furthermore, we tested therapeutic options targeting ATX in a related mouse line. Using EEG combined with TMS in an instructed fear paradigm, neuropsychological analysis and an fMRI based episodic memory task, we found intermediate phenotypes of mental disorders in human carriers of a loss-of-function single nucleotide polymorphism of PRG-1 (PRG-1R345T/WT). Prg-1R346T/WT animals phenocopied human carriers showing increased anxiety, a depressive phenotype and lower stress resilience. Network analysis revealed that coherence and phase-amplitude coupling were altered by PRG-1 deficiency in memory related circuits in humans and mice alike. Brain oscillation phenotypes were restored by inhibtion of ATX in Prg-1 deficient mice indicating an interventional potential for mental disorders.

Importance:Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified. Objective:To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD. Design, Setting, and Participants:This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023. Exposure:Patients with MDD and healthy controls. Main Outcome and Measure:Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression. Results:Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups. Conclusion and Relevance:Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.

AbstractMajor Depressive Disorder (MDD) is a highly prevalent, severe mental health condition that constitutes one of the leading causes of disability worldwide. While recent animal studies suggest a causal role of the gut microbiome in the pathophysiology of MDD models, evidence in humans is still unclear due to small sample sizes, inconsistent clinical assessment of MDD diagnosis, and methodological limitations regarding causal inference in cross-sectional data. Here, we explicitly address these shortcomings to investigate the potential causal link between the gut microbiome and MDD: First, we replicate previous findings using one of the largest multicenter MDD cohorts for which microbiome data and in-depth diagnostic assessment are available (N=1,269 MDD patients and controls). We find a significant difference between healthy controls and MDD patients for the relative abundance of the four taxaEggerthella,Hungatella,Coprobacillus, andLachnospiraceaeFCS020. Second, we employ state-of-the-art, fully data-driven causal inference tools within Judea Pearl’s framework, allowing us to derive model constraints from the data rather than relying on potentially strong, unrealistic assumptions. Using this approach, we found data-driven evidence forEggerthella and Hungatellaas causal contributors to MDD. Furthermore, we show thatEggerthellaandHungatellaabundances are associated with MDD beyond the influence of body mass index, identifying two distinct pathways linking MDD to the gut microbiome. Finally, the difference in relative abundance of these taxa between healthy and MDD patients was independent of antidepressant medication. Our study provides the first evidence for a potential causal role of gut-microbiota in the pathophysiology of depression in humans.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Decoding human speech requires the brain to segment the incoming acoustic signal into meaningful linguistic units, ranging from syllables and words to phrases. Integrating these linguistic constituents into a coherent percept sets the root of compositional meaning and hence understanding. One important cue for segmentation in natural speech are prosodic cues, such as pauses, but their interplay with higher-level linguistic processing is still unknown. Here we dissociate the neural tracking of prosodic pauses from the segmentation of multi-word chunks using magnetoencephalography (MEG). We find that manipulating the regularity of pauses disrupts slow speech-brain tracking bilaterally in auditory areas (below 2 Hz) and in turn increases left-lateralized coherence of higher frequency auditory activity at speech onsets (around 25 - 45 Hz). Critically, we also find that multi-word chunks—defined as short, coherent bundles of inter-word dependencies—are processed through the rhythmic fluctuations of low frequency activity (below 2 Hz) bilaterally and independently of prosodic cues. Importantly, low-frequency alignment at chunk onsets increases the accuracy of an encoding model in bilateral auditory and frontal areas, while controlling for the effect of acoustics. Our findings provide novel insights into the neural basis of speech perception, demonstrating that both acoustic features (prosodic cues) and abstract processing at the multi-word timescale are underpinned independently by low-frequency electrophysiological brain activity.### Competing Interest StatementThe authors have declared no competing interest.

Decoding human speech requires the brain to segment the incoming acoustic signal into meaningful linguistic units, ranging from syllables and words to phrases. Integrating these linguistic constituents into a coherent percept sets the root of compositional meaning and hence understanding. One important cue for segmentation in natural speech is prosodic cues, such as pauses, but their interplay with higher-level linguistic processing is still unknown. Here, we dissociate the neural tracking of prosodic pauses from the segmentation of multi-word chunks using magnetoencephalography (MEG). We find that manipulating the regularity of pauses disrupts slow speech-brain tracking bilaterally in auditory areas (below 2 Hz) and in turn increases left-lateralized coherence of higher-frequency auditory activity at speech onsets (around 25-45 Hz). Critically, we also find that multi-word chunks-defined as short, coherent bundles of inter-word dependencies-are processed through the rhythmic fluctuations of low-frequency activity (below 2 Hz) bilaterally and independently of prosodic cues. Importantly, low-frequency alignment at chunk onsets increases the accuracy of an encoding model in bilateral auditory and frontal areas while controlling for the effect of acoustics. Our findings provide novel insights into the neural basis of speech perception, demonstrating that both acoustic features (prosodic cues) and abstract linguistic processing at the multi-word timescale are underpinned independently by low-frequency electrophysiological brain activity in the delta frequency range.

Up to 70% of patients with major depressive disorder present with psychomotor disturbance (PmD), but at the present time understanding of its pathophysiology is limited. In this study, we capitalized on a large sample of patients to examine the neural correlates of PmD in depression. This study included 820 healthy participants and 699 patients with remitted ( n = 402) or current ( n = 297) depression. Patients were further categorized as having psychomotor retardation, agitation, or no PmD. We compared resting-state functional connectivity (ROI-to-ROI) between nodes of the cerebral motor network between the groups, including primary motor cortex, supplementary motor area, sensory cortex, superior parietal lobe, caudate, putamen, pallidum, thalamus, and cerebellum. Additionally, we examined network topology of the motor network using graph theory. Among the currently depressed 55% had PmD (15% agitation, 29% retardation, and 11% concurrent agitation and retardation), while 16% of the remitted patients had PmD (8% retardation and 8% agitation). When compared with controls, currently depressed patients with PmD showed higher thalamo-cortical and pallido-cortical connectivity, but no network topology alterations. Currently depressed patients with retardation only had higher thalamo-cortical connectivity, while those with agitation had predominant higher pallido-cortical connectivity. Currently depressed patients without PmD showed higher thalamo-cortical, pallido-cortical, and cortico-cortical connectivity, as well as altered network topology compared to healthy controls. Remitted patients with PmD showed no differences in single connections but altered network topology, while remitted patients without PmD did not differ from healthy controls in any measure. We found evidence for compensatory increased cortico-cortical resting-state functional connectivity that may prevent psychomotor disturbance in current depression, but may perturb network topology. Agitation and retardation show specific connectivity signatures. Motor network topology is slightly altered in remitted patients arguing for persistent changes in depression. These alterations in functional connectivity may be addressed with non-invasive brain stimulation.

When we attentively listen to an individual's speech, our brain activity dynamically aligns to the incoming acoustic input at multiple timescales. Although this systematic alignment between ongoing brain activity and speech in auditory brain areas is well established, the acoustic events that drive this phase-locking are not fully understood. Here, we use magnetoencephalographic recordings of 24 human participants (12 females) while they were listening to a 1 h story. We show that whereas speech-brain coupling is associated with sustained acoustic fluctuations in the speech envelope in the theta-frequency range (4-7 Hz), speech tracking in the low-frequency delta (below 1 Hz) was strongest around onsets of speech, like the beginning of a sentence. Crucially, delta tracking in bilateral auditory areas was not sustained after onsets, proposing a delta tracking during continuous speech perception that is driven by speech onsets. We conclude that both onsets and sustained components of speech contribute differentially to speech tracking in delta- and theta-frequency bands, orchestrating sampling of continuous speech. Thus, our results suggest a temporal dissociation of acoustically driven oscillatory activity in auditory areas during speech tracking, providing valuable implications for orchestration of speech tracking at multiple time scales.

The Phospholipid Phosphatase Related 4 gene (PLPPR4,  *607813) encodes the Plasticity-Related-Gene-1 (PRG-1) protein. This cerebral synaptic transmembrane-protein modulates cortical excitatory transmission on glutamatergic neurons. In mice, homozygous Prg-1 deficiency causes juvenile epilepsy. Its epileptogenic potential in humans was unknown. Thus, we screened 18 patients with infantile epileptic spasms syndrome (IESS) and 98 patients with benign familial neonatal/infantile seizures (BFNS/BFIS) for the presence of PLPPR4 variants. A girl with IESS had inherited a PLPPR4-mutation (c.896C > G, NM_014839; p.T299S) from her father and an SCN1A-mutation from her mother (c.1622A > G, NM_006920; p.N541S). The PLPPR4-mutation was located in the third extracellular lysophosphatidic acid-interacting domain and in-utero electroporation (IUE) of the Prg-1p.T300S construct into neurons of Prg-1 knockout embryos demonstrated its inability to rescue the electrophysiological knockout phenotype. Electrophysiology on the recombinant SCN1Ap.N541S channel revealed partial loss-of-function. Another PLPPR4 variant (c.1034C > G, NM_014839; p.R345T) that was shown to result in a loss-of-function aggravated a BFNS/BFIS phenotype and also failed to suppress glutamatergic neurotransmission after IUE. The aggravating effect of Plppr4-haploinsufficiency on epileptogenesis was further verified using the kainate-model of epilepsy: double heterozygous Plppr4-/+|Scn1awt|p.R1648H mice exhibited higher seizure susceptibility than either wild-type, Plppr4-/+, or Scn1awt|p.R1648H littermates. Our study shows that a heterozygous PLPPR4 loss-of-function mutation may have a modifying effect on BFNS/BFIS and on SCN1A-related epilepsy in mice and humans.