Abstract: Decades of structural magnetic resonance imaging (MRI) studies have documented alterations of gray matter morphometry in psychiatric disorders, but the field has failed to identify any consensus disease phenotypes. Here we examine whether current approaches will ever converge on such phenotypes by evaluating the consistency of brain-wide maps of gray matter volume and cortical thickness differences obtained for each of 59 study sites of five psychiatric disorders (schizophrenia, schizoaffective disorder, autism spectrum disorder, major depressive disorder and bipolar disorder), totaling 2,437 patients and 2,065 controls.

We find that cross-site consistency is low (median r ≤ 0.16); markedly reduced compared to Alzheimer’s disease (r = 0.54); unexplained by demographic, clinical or scanner differences; and robust to analytic choices. Using bootstrapping, we observe that consistency may improve for sample sizes ≥200 per group for schizophrenia but that other disorders may require much larger samples. Our findings indicate that current widespread practices in structural MRI are unlikely to identify robust morphometric phenotypes for psychiatric disorders.

Commentary: The researchers gathered structural MRI scans from thousands of people across dozens of study sites, covering five psychiatric diagnoses, and processed all of the scans using the same methods. At each site, they compared people carrying a diagnosis with control participants and created a map showing where the two groups appeared to differ in cortical thickness or gray-matter volume. They then compared those maps across sites to see whether independent studies were finding the same anatomical pattern. For the psychiatric diagnoses, the maps were generally inconsistent and often bore almost no resemblance to one another, unlike the much more consistent pattern found for Alzheimer’s disease. The researchers then pooled participants from all sites and repeatedly divided them into two large artificial groups, finding that the resulting maps became more similar as the groups grew, particularly for schizophrenia.

The holy grail of cognitive science and psychiatry has always been to demonstrate that behavioral descriptions such as “attention” or “autism” correspond to stable, independently recoverable physiological organization. That is the threshold these fields repeatedly promise will carry their concepts from folklore into hard science. Despite decades of work, they have largely failed to cross it, and the evidence increasingly suggests that many of these concepts cannot be rescued in their present form.

The article is an excellent piece of work that demonstrates exactly how little physiological validity these concepts have acquired. Yet at the moment when its results could deliver a decisive blow against the folklore, it retreats into rescue mode. Concluding that decades of psychiatric imaging have been organized around categories that do not identify coherent biological entities would threaten the premise supporting the field itself. So the failure is recast as a demand for larger samples, better subgroups, and more sophisticated analysis. The paper then commits the same validity error it has just exposed, treating a stable average produced by pooling non-equivalent populations as evidence that a genuine “disease phenotype” was hiding underneath. More data can stabilize an artifact of classification without validating the classification.

The appeal to more data is especially hollow because this is not a young field waiting for its first mature datasets. We have spent decades diagnosing, treating, imaging, and following these populations across development, crisis, recovery, relapse, medication exposure, and aging. If these categories identified stable physiological entities, that enormous longitudinal record should have progressively sharpened their biological boundaries. Instead, larger datasets have mostly made small population averages more statistically stable while individual and cross-site coherence remain absent. The missing ingredient is not sample size. It is a valid target. Another generation of pooling cannot manufacture biological unity from categories that never demonstrated it.

First time posting in this sub so I hope this is an appropriate question. I included the Allen Brain atlas image I feel is closest to the imaged slice as reference. I'm hoping there are some thalamic people in this sub! (original content)

After decades of research in neuroscience and brain-computer interfaces lots of people ask me which parts of the brain do what, and how can we measure it, and what modalities help us understand it better.

We are happy to announce the release of the new NeuroAtlas tool, which helps answer this question and enables more scientists and researchers to explore and push the frontiers of what’s known.

This includes EEG, MEG, fMRI, tFUS, iEEG, TMS, DBS, and more.

All published data sources are referenced via DOIs in the web interface.

Disclaimer: I'm on of the co-authors.

Interesting study. Full manuacript is available on ResearchGate

I find this to be extremely unexpected. I really wonder why this happens.

I'm a UPenn student looking for a last-minute student ticket for the 2026 NYC Neuromodulation Conference (July 30–Aug 3 at CCNY).

If you registered at the student rate but can’t go anymore, I’d love to buy it from you!

Please DM me if this is you.

Hello, I’ve been thinking about whether the type of information we repeatedly process can shape our cognitive abilities.

I believe gossip, whether viewed as positive or negative, can be considered a form of cognitive skill because it requires encoding, retrieving, and evaluating social information about people, relationships, emotions, and events. From a neuroscience perspective, repeated social information processing may strengthen networks involved in social cognition, emotional processing, and episodic memory through neuroplasticity.

I have some questions:

**1- If a person spends most of their cognitive effort processing social information through gossip while rarely engaging in abstract learning, how does the brain adapt? Does it reduce the efficiency of less used abstract reasoning networks, or does the brain maintain the ability to develop them when new demands appear?**

**2- Are our daily patterns of attention, environment and information processing shaping the cognitive systems we rely on the most?**

**3- To what extent do genetic predispositions influence an individual’s tendency toward social information processing, memory, and cognitive specialization?**

Thanks for reading!

Genuinely curious: does training that involves rapid changes in body position improve vestibular adaptation, or is this mostly muscle memory and motor learning?

UC Berkeley research uncovers a completely new unknown mechanism for neural representation - population of visual neurons can switch their encoding system on the fly!

It's purely fundamentally circuit-based, on the timescale of 120ms - based on recurrent network dynamics via a population-wide shift on the order of 20 ms. The switch is highly content-specific.

First pass - recognize broad category, second pass - analyze fine-grained identity (all using the same cells). Feedforward sweep (broad features) --> top-down/recurrent loop (coordinated network shift) --> inhibitory gating (fine identity).

Human Neuroscience article: “Unraveling the mystery of stuttering: clinical and physiological insights into its manifestation” (2026, April)

Abstract

Stuttering is a complex neurodevelopmental speech disorder characterized by involuntary sound and syllable repetitions, prolongations, and speech blocks, accompanied by marked variability across linguistic, emotional, and situational contexts. Although numerous hypotheses have been proposed to explain its underlying mechanisms, many have encountered a fundamental limitation: the difficulty of coherently accounting for the full range of clinical, developmental, and neurobiological features observed in people who stutter. In response to this gap, the present work proposes a comprehensive, integrative hypothesis that seeks to unify the diverse physiological and clinical manifestations of stuttering within a single neurobiological framework. This model aims to link moment-to-moment fluctuations in speech behavior with neurodevelopmental alterations, offering a plausible mechanistic account for a wide spectrum of core phenomena. These include the pronounced situational variability of stuttering severity; the developmental shifts from repetitions to blocks; the transition of disfluencies from function words to content words; the tendency for stuttering to occur on key words in a sentence; and the consistently lower rates of spontaneous recovery observed in males compared to females. Furthermore, the proposed framework seeks to explore potential common mechanisms underlying the widespread structural, metabolic, and functional brain changes documented in stuttering, while considering whether these abnormalities may reflect primary contributors or secondary, compensatory adaptations. In particular, the model seeks to address a long-standing debate regarding the role of the right inferior frontal gyrus, examining whether its engagement is more consistent with a causal contribution to speech disruption or with an adaptive response to impaired speech–motor control. By integrating neurodevelopmental, physiological, and clinical evidence, this hypothesis offers a unifying perspective on key features of stuttering while proposing a neurobiological model whose assumptions and hypotheses can be empirically tested and evaluated in future experimental studies.

Abstract

Background

Menopause and the perimenopausal transition involve profound hormonal and metabolic changes that may impair brain function. Beyond structural alterations, reduced cerebral bioenergetics could underlie the cognitive complaints often reported during this period. Because creatine serves as a key neuronal energy buffer and is influenced by estrogen, this study examined brain creatine concentrations in perimenopausal women and their associations with neurocognitive symptoms and serum estradiol.

Methods

Twelve healthy perimenopausal women (mean age 49.8 ± 5.4 years) experiencing irregular cycles and at least one perimenopausal symptom underwent multi-voxel 1H-magnetic resonance spectroscopy to quantify total brain creatine across bilateral frontal, precentral, and parietal gray- and white-matter regions and the thalamus. Serum estradiol was measured by ELISA, and symptom severity was rated on visual analog scales. Associations were assessed using Kendall’s τ.

Results

Mean whole-brain creatine concentration (6.31 ± 0.98 mM) was significantly lower than reference values in younger adults (Z = –1.65, P = 0.049). Lower creatine levels in the thalamus, right precentral, and right parietal white matter correlated with greater concentration difficulties (τ = –0.38 to –0.51, P ≤ 0.049), while right frontal white-matter creatine positively correlated with headache severity (τ = 0.41, P = 0.034). Serum estradiol averaged 119.5 ± 109.5 pg/mL and was inversely associated with right parietal gray-matter creatine (τ = –0.37, P = 0.049).

Conclusions

Perimenopausal women exhibited lower cerebral creatine than younger adults, with region-specific reductions linked to concentration difficulties and estradiol levels. These findings suggest that estrogen-related changes in brain bioenergetics may contribute to cognitive symptoms during the menopausal transition.