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The fundamental role of the brain-specific myelin transcription factor 1-like (MYT1L) gene in cases of intellectual disability and in the etiology of neurodevelopmental disorders is increasingly recognized. Yet, its function remains under-investigated. Here, we identify a network of helix-loop-helix (HLH) transcriptional regulators controlled by MYT1L, as indicated by our analyses in human neural stem cells and in the human brain. Using cell-based knockdown approaches and microarray analyses we found that (1) MYT1L is required for neuronal differentiation and identified ID1, a HLH inhibitor of premature neurogenesis, as a target. (2) Although MYT1L prevented expression of ID1, it induced expression of a large number of terminal differentiation genes. (3) Consistently, expression of MYT1L in the human brain coincided with neuronal maturation and inversely correlated with that of ID1 and ID3 throughout the lifespan. (4) Genetic polymorphisms that reduced expression of MYT1L in the hippocampus resulted in increased expression of ID1 and ID3, decreased levels of the proneural basic HLH (bHLH) transcriptional regulators TCF4 and NEUROD6 and decreased expression of genes involved in long-term potentiation and synaptic transmission, cancer and neurodegeneration. Furthermore, our neuroimaging analyses indicated that MYT1L expression associated with hippocampal volume and activation during episodic memory recall, as measured by blood-oxygen-level-dependent (BOLD) signals. Overall, our findings suggest that MYT1L influences memory-related processes by controlling a neuronal proliferation/differentiation switch of ID-bHLH factors.

Alzheimer's disease is a devastating, common, progressive dementia with considerable heritability. Recently, a genetic variant associated with the disease was discovered at CLU (rs11136000) with genome-wide support. Here we show, using an imaging genetics approach in a large genotyped sample, that healthy carriers of the variant exhibit altered coupling between hippocampus and prefrontal cortex during memory processing, mirroring clinical evidence of disturbed connectivity in patients and providing a neurogenetic mechanism for CLU-associated risk and protection.

Healthy aging has been shown to modulate the neural circuitry underlying simple declarative memory; however, the functional impact of negative stimulus valence on these changes has not been fully investigated. Using BOLD fMRI, we explored the effects of aging on behavioral performance, neural activity, and functional coupling during the encoding and retrieval of novel aversive and neutral scenes. Behaviorally, there was a main effect of valence with better recognition performance for aversive greater than neutral stimuli in both age groups. There was also a main effect of age with better recognition performance in younger participants compared to older participants. At the imaging level, there was a main effect of valence with increased activity in the medial-temporal lobe (amygdala and hippocampus) during both encoding and retrieval of aversive relative to neutral stimuli. There was also a main effect of age with older participants showing decreased engagement of medial-temporal lobe structures and increased engagement of prefrontal structures during both encoding and retrieval sessions. Interestingly, older participants presented with relatively decreased amygdalar-hippocampal coupling and increased amygdalar-prefrontal coupling when compared to younger participants. Furthermore, older participants showed increased activation in prefrontal cortices and decreased activation in the amygdala when contrasting the retrieval of aversive and neutral scenes. These results suggest that although normal aging is associated with a decline in declarative memory with alterations in the neural activity and connectivity of brain regions underlying simple declarative memory, memory for aversive stimuli is relatively better preserved than for neutral stimuli, possibly through greater compensatory prefrontal cortical activity.

The human brain's capacity for cognitive function is thought to depend on coordinated activity in sparsely connected, complex networks organized over many scales of space and time. Recent work has demonstrated that human brain networks constructed from neuroimaging data have economical small-world properties that confer high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of complex brain networks functioning at different frequencies can be related to behavioral performance on cognitive tasks. Here, we show that impaired accuracy of working memory could be related to suboptimal cost efficiency of brain functional networks operating in the classical beta frequency band, 15-30 Hz. We analyzed brain functional networks derived from magnetoencephalography data recorded during working-memory task performance in 29 healthy volunteers and 28 people with schizophrenia. Networks functioning at higher frequencies had greater global cost efficiency than low-frequency networks in both groups. Superior task performance was positively correlated with global cost efficiency of the beta-band network and specifically with cost efficiency of nodes in left lateral parietal and frontal areas. These results are consistent with biophysical models highlighting the importance of beta-band oscillations for long-distance functional connections in brain networks and with pathophysiological models of schizophrenia as a dysconnection syndrome. More generally, they echo the saying that "less is more": The information processing performance of a network can be enhanced by a sparse or low-cost configuration with disproportionately high efficiency.

Using the multivariate long memory (LM) model and Taylor expansions, we find the conditions for convergence of the wavelet correlations between two LM processes on an asymptotic value at low frequencies. These mathematical results, and a least squares estimator of LM parameters, are validated in simulations and applied to neurophysiological (human brain) and financial market time series. Both brain and market systems had multivariate LM properties including a "fractal connectivity" regime of scales over which wavelet correlations were invariantly close to their asymptotic value. This analysis provides efficient and unbiased estimation of long-term correlations in diverse dynamic networks.

Dopaminergic and glutamatergic systems are critical components responsible for prefrontal signal-to-noise tuning in working memory. Recent functional MRI (fMRI) studies of genetic variation in these systems in catechol-O-methyltransferase (COMT) and in metabotropic glutamate receptor mgluR3 (GRM3), respectively, suggest that these genes influence prefrontal physiological signal-to-noise in humans. Here, using fMRI, we extend these individual gene findings to examine the combined effects of COMT and GRM3 on dissociable components of the frontoparietal working memory network. We observed an apparent epistatic interaction of these two genes on the engagement of prefrontal cortex during working memory. Specifically, the GRM3 genotype putatively associated with suboptimal glutamatergic signaling was significantly associated with inefficient prefrontal engagement and altered prefrontal-parietal coupling on the background of COMT Val-homozygous genotype. Conversely, COMT Met-homozygous background mediated against the effect of GRM3 genotype. These findings extend putative brain dopaminergic and glutamatergic relationships indexed by COMT and GRM3 to a systems-level interaction in human cortical circuits implicated in working memory dysfunction such as in schizophrenia.

Altered connectivity of dorsolateral prefrontal cortex (DLPFC) and hippocampus during working memory is considered an intermediate phenotype for schizophrenia (SCZ), but the relevance for other mental disorders with shared genetic background remains unknown. Here we investigated its presence in unaffected first-degree relatives of patients with bipolar disorder (BD) or major depressive disorder (MDD). Furthermore, we aimed to provide an independent replication of this phenotype in first-degree relatives of SCZ patients. We acquired functional magnetic resonance imaging (fMRI) data from 309 healthy controls and 218 healthy first-degree relatives of index patients with SCZ (n = 62), BD (n = 66) and MDD (n = 90), who completed the n-back working memory paradigm. We observed a significant group effect on DLPFC--hippocampus coupling (PFWE = .031, all P-values region of interest [ROI] corrected). Post hoc comparisons revealed that this effect was driven by the SCZ relatives, who showed a significant increase in the negative functional connectivity of the DLPFC and right hippocampus compared to controls (PFWE = .001), BD relatives (PFWE = .015) and trend-wise also MDD relatives (PFWE = .082). Comparison of BD and MDD relatives to the controls revealed no difference (PFWE- values > .451). Supplementary analyses suggested that the SCZ relatives finding is robust to a range of potential confounds, including structural differences. Our data further support altered DLPFC--hippocampus connectivity during working memory as an intermediate phenotype for SCZ. This suggests that this phenotype is relatively specific to SCZ and does not translate to other genetically related disorders in the mood-psychosis spectrum. [ABSTRACT FROM AUTHOR]

A variation in the BDNF gene (val66met) affects the function of BDNF in neurons, predicts variation in human memory, and is associated with several neurological and psychiatric disorders. Here, we show that, in magnetic resonance imaging scans of a large sample of normal individuals, this polymorphism affects the anatomy of the hippocampus and prefrontal cortex, identifying a genetic mechanism of variation in brain morphology related to learning and memory. [ABSTRACT FROM AUTHOR]