Visual Brain Research Articles

Visual attention modulates mu suppression during biological motion perception in autistic individuals

AbstractThere has been a lot of controversy regarding mirror neuron function in autism spectrum disorder (ASD), in particular during the observation of biological motions (BM). Here, we directly explored the link between visual attention and brain activity in terms of mu suppression, by simultaneously recording eye‐tracking and EEGs during BM tasks. Nineteen autistic children (15 boys, mean age = 11.57 ± 4.28 years) and 19 age‐matched neurotypical (NT) children (15 boys, mean age = 11.68 ± 5.22 years) participated in the study. Each participant's eye movement and EEG were simultaneously recorded while watching four BM stimuli (walking, cartwheeling, free‐throwing and underarm throwing) and a scrambled condition. Mu (8–13 Hz) suppression index (SI) for central regions was calculated. Fixation counts and percent of fixation time were calculated as indices of eye movements. EEG results revealed significant mu suppressions in the central region in both groups for all BM actions. Eye‐tracking results showed that NT children had greater fixation counts and a higher percentage of fixation time than autistic children, indicating greater overall visual attention to BM. Notably, correlational analyses for both groups further revealed that individuals' fixation time and fixation counts were negatively correlated with the mu suppression index for all actions, indicating a strong association between visual attention and mu SI in the central region. Our findings suggest a critical role of visual attention in interpreting mu suppression during action perception in autism.

A large-scale examination of inductive biases shaping high-level visual representation in brains and machines

The rapid release of high-performing computer vision models offers new potential to study the impact of different inductive biases on the emergent brain alignment of learned representations. Here, we perform controlled comparisons among a curated set of 224 diverse models to test the impact of specific model properties on visual brain predictivity – a process requiring over 1.8 billion regressions and 50.3 thousand representational similarity analyses. We find that models with qualitatively different architectures (e.g. CNNs versus Transformers) and task objectives (e.g. purely visual contrastive learning versus vision- language alignment) achieve near equivalent brain predictivity, when other factors are held constant. Instead, variation across visual training diets yields the largest, most consistent effect on brain predictivity. Many models achieve similarly high brain predictivity, despite clear variation in their underlying representations – suggesting that standard methods used to link models to brains may be too flexible. Broadly, these findings challenge common assumptions about the factors underlying emergent brain alignment, and outline how we can leverage controlled model comparison to probe the common computational principles underlying biological and artificial visual systems.

Enabling Electric Field Model of Microscopically Realistic Brain.

Modeling brain stimulation at the microscopic scale may reveal new paradigms for a variety of stimulation modalities. We present the largest map of distributions of the extracellular electric field to date within a layer L2/L3 mouse primary visual cortex brain sample, which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250×140×90 μm 3 volume). We used the map to identify microscopic perturbations of the extracellular electric field and their effect on the activating thresholds of individual neurons. Previous relevant studies modeled a macroscopically homogeneous cortical volume. Result: Our immediate result is a reduction of the predicted stimulation field strength necessary for neuronal activation by a factor of approximately 0.7 (or by 30%) on average, due to microscopic perturbations of the extracellular electric field-an electric field "spatial noise" with a mean value of zero. Although this result is largely sample-specific, it aligns with experimental data indicating that existing macroscopic theories substantially overestimate the electric fields necessary for brain stimulation. Currently, there is a discrepancy between macroscopic volumetric brain modeling for brain stimulation and experimental results: experiments typically reveal lower electric intensities required for brain stimulation. This study is arguably the first attempt to model brain stimulation at the microscopic scale, enabled by automated analysis of modern scanning electron microscopy images of the brain. The immediate result is a prediction of lower electric field intensities necessary for brain stimulation, with an average reduction factor of 0.7.

Development and assessment of algorithms for predicting brain amyloid positivity in a population without dementia

BackgroundThe accumulation of amyloid-β (Aβ) peptide in the brain is a hallmark of Alzheimer’s disease (AD), occurring years before symptom onset. Current methods for quantifying in vivo amyloid load involve invasive or costly procedures, limiting accessibility. Early detection of amyloid positivity in non-demented individuals is crucial for aiding early AD diagnosis and for initiating anti-amyloid immunotherapies at early stages. This study aimed to develop and validate predictive models to identify brain amyloid positivity in non-demented patients, using routinely collected clinical data.MethodsPredictive models for amyloid positivity were developed using data from 853 non-demented participants in the MEMENTO cohort. Amyloid levels were measured potentially repeatedly during study course through Positron Emision Tomography or CerebroSpinal Fluid analysis.The probability of amyloid positivity was modelled using mixed-effects logistic regression. Predictors included demographic information, cognitive assessments, visual brain MRI evaluations of hippocampal atrophy and lobar microbleeds, AD-related blood biomarkers (Aβ42/40 and P-tau181), and ApoE4 status. Models were subjected to internal cross-validation and external validation using data from the Amsterdam Dementia Cohort. Performance also was evaluated in a subsample that met the main criteria of the Appropriate Use Recommendations (AUR) for lecanemab.ResultsThe most effective model incorporated demographic data, cognitive assessments, ApoE status, and AD-related blood biomarkers, achieving AUCs of 0.82 [95%CI 0.81-0.82] in MEMENTO sample and 0.90 [95%CI 0.86-0.94] in the external validation sample. This model significantly outperformed a reference model based solely on demographic and cognitive data, with an AUC difference in MEMENTO of 0.10 [95%CI 0.10-0.11]. A similar model without ApoE genotype achieved comparable discriminatory performance. MRI markers did not improve model performance. Performances in AUR of lecanemab subsample were comparable.ConclusionA predictive model integrating demographic, cognitive, and blood biomarker data offers a promising method to help identify amyloid status in non-demented patients. ApoE genotype and brain MRI data were not necessary for strong discriminatory ability, suggesting that ApoE genotyping may be deferred when assessing the risk-benefit ratio of immunotherapies in amyloid-positive patients who desire treatment. The integration of this model into clinical practice could reduce the need for lumbar puncture or PET examinations to confirm amyloid status.

Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method.

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

Neuro-cognitive effects of degraded visibility on illusory body ownership

Based on visuo-tactile stimulation, the rubber hand illusion induces a sense of ownership for a dummy hand. Manipulating the visibility of the dummy hand during the stimulation influences cognitive aspects of the illusion, suggesting that the related brain activity may be influenced too. To test this, we analyzed brain activity (fMRI), subjective ratings, and skin conductance from 45 neurotypical participants undergoing a modified rubber hand illusion protocol where we manipulated the visibility (high, medium, and low) of a virtual hand, not the brush (virtual hand illusion; VHI). To further investigate the impact of visibility manipulations on VHI-related secondary effects (i.e. vicarious somatosensation), we recorded brain activity and skin conductance during a vicarious pain protocol (observation of painful stimulations of the virtual hand) that occurred after the VHI procedure. Results showed that, during both the VHI and vicarious pain periods, the activity of distinct visual, somatosensory, and motor brain regions was modulated by (i) visibility manipulations, (ii) coherence between visual and tactile stimulation, and (iii) time of visuo-tactile stimulation. Accordingly, embodiment-related subjective ratings of the perceived illusion were specifically influenced by visibility manipulations. These findings suggest that visibility modifications can impact the neural and cognitive effects of illusory body ownership, in that when visibility decreases the illusion is perceived as weaker and the brain activity in visual, motor, and somatosensory regions is overall lower. We interpret this evidence as a sign of the weight of vision on embodiment processes, in that the cortical and subjective aspects of illusory body ownership are weakened by a degradation of visual input during the induction of the illusion.

Environmental Lighting Conditions, Phenomenal Contrast, and the Conscious Perception of Near and Far.

Recent evidence in systems neuroscience suggests that lighting conditions affect the whole chain of brain processing, from retina to high-level cortical networks, for perceptual and cognitive function. Here, visual adaptation levels to three different environmental lighting conditions, (1) darkness, (2) daylight, and (3) prolonged exposure to very bright light akin to sunlight, were simulated in lab to investigate the effects of light adaptation levels on classic cases of subjective contrast, assimilation, and contrast-induced relative depth in achromatic, i.e., ON-OFF pathway mediated visual configurations. After adaptation/exposure to a given lighting condition, configurations were shown in grouped and ungrouped conditions in random order to healthy young humans in computer-controlled two-alternative forced-choice procedures that consisted of deciding, as quickly as possible, which of two background patterns in a given configuration of achromatic contrast appeared lighter, or which of two foreground patterns appeared to stand out in front, as if it were nearer to the observer. We found a statistically significant effect of the adaptation levels on the consciously perceived subjective contrast (F(2,23) = 20.73; p < 0.001) and the relative depth (F(2,23) = 12.67; p < 0.001), a statistically significant interaction between the adaptation levels and the grouping factor (F(2,23) = 4.73; p < 0.05) on subjective contrast, and a statistically significant effect of the grouping factor on the relative depth (F(2,23) = 13.71; p < 0.01). Visual adaption to different lighting conditions significantly alters the conscious perception of contrast and assimilation, classically linked to non-linear functional synergies between ON and OFF processing channels in the visual brain, and modulates the repeatedly demonstrated effectiveness of luminance contrast as a depth cue; the physically brighter pattern regions in the configurations are no longer consistently perceived as nearer to a conscious observer under daylight and extreme bright light adapted (rod-saturated) conditions.

The effect of interstimulus interval on sustained attention

The ability of nervous systems to filter out irrelevant and repetitive stimuli may prevent animals from becoming ‘saturated’ with excess information. However, animals must be particular about which stimuli to attend to and which to ignore, as mistakes may be costly. Using a comparative approach, we explored the effect of interstimulus interval (ISI) between repeated presentations of visual stimuli presented on a screen to test the decrease in responses (response decrement) of both Trite planiceps jumping spiders and untrained Columba livia pigeons, animals with comparable visual ability despite having structurally different visual systems and brain size. We used ISIs of 2.5 s, 5 s, 10 s, predicting that decreases in ISI would lead to progressively less responses to the stimuli. Following from previous work on T. planiceps, we also manipulated pigeon hunger level, finding that hungry birds were initially more responsive than sated pigeons, but the rate of decrease in responses to the stimulus did not differ between the two groups. While a clear response decrement was seen in both species across all conditions, shorter ISIs resulted in more dramatic response decrements, aligning with previous work and with the resource depletion theory posited in the human-based literature.

Assessment of pediatric visual acuity

The VA assessment of children within the different age groups is a very tough task somewhat due to of cooperation of children. However, the method of estimation of pediatric VA is based on different types of concern according to age. VA is a compound function of the minimum visibility, resolution, recognition and minimum discriminability. Sometimes the VA is hampered due to the types of refractive error and the initial correction of the error is also an important issue. The 5 to 6 years of age children with normal growth respond easily and they can be observed with the adult method but the youngest school-age children can be estimated with nonverbal methods. The growth of social and educational development of the children can retard due to acuity impairment but early detection and visual correction enhances the issue.The pathological disorders in childhood like corneal and lenticular disorders, macular degeneration, tumours, multiple sclerosis etc., turning of the eye from the standard called strabismus and it happens due to the reduction of the VA and the loss of binocular vision. Thus visual acuity is an approach for the assessment of ocular health along with the visual brain and its pathway.

Distinct tactile and visual brain responses to alphabetic letters converge to shared representations in blind and sighted readers

Distinct tactile and visual brain responses to alphabetic letters converge to shared representations in blind and sighted readers

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.

Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of 'predatory'-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.

From Cats to the Cortex: Unravelling the Hierarchical Processing System of Vision and Brain Plasticity.

The groundbreaking research conducted by neurophysiologists David Hubel and Torsten Wiesel during the late 1950s and 1960s revolutionized the field of visual neuroscience. Through single-unit recordings in the visual cortex of cats, they made several key discoveries that fundamentally changed our understanding of visual processing. Their work introduced the concept of orientation selectivity, revealing that neurons in the visual cortex are specifically tuned to line orientations, thereby illustrating how the brain constructs visual representations through edge detection. Additionally, they discovered ocular dominance columns, the specialized cortical regions that respond preferentially to input from one eye, providing crucial insights into the organization of visual processing and the importance of binocular vision. Hubel and Wiesel's research also established the concept of a critical period in visual development, demonstrating that early visual experiences are essential for the proper maturation of the visual system. This discovery has had significant implications for understanding neural plasticity and the role of sensory input in neural development. The impact of their work goes beyond theoretical knowledge, contributing to the development of therapeutic strategies for some visual disorders and guiding current research into brain plasticity and visual processing. This review synthesizes the monumental contributions of Hubel and Wiesel, evaluating how their key discoveries have shaped subsequent research in visual neuroscience. It traces the evolution of knowledge related to visual pathways, feature detection, and brain plasticity, highlighting the enduring influence of their foundational work on contemporary studies. By exploring the progression from their pioneering findings to modern advancements, this review emphasizes the legacy of Hubel and Wiesel's contributions to our understanding of vision and neural function.

Glutamate dynamics and BOLD response during OCD symptom provocation in the lateral occipital cortex: A 7 Tesla fMRI-fMRS study

Obsessive-compulsive disorder (OCD) is linked with dysfunction in frontal-striatal, fronto-limbic, and visual brain regions. Research using proton magnetic resonance spectroscopy (1H-MRS) suggests that altered neurometabolite levels, like glutamate, may contribute to this dysfunction. However, static neurometabolite levels in OCD patients have shown inconsistent results, likely due to previous studies' limited focus on neurometabolite dynamics. We employ functional MRS (fMRS) and functional magnetic resonance imaging (fMRI) to explore these dynamics and brain activation during OCD symptom provocation.We utilized a combined 7-tesla fMRI-fMRS setup to examine task-related BOLD response and glutamate changes in the lateral occipital cortex (LOC) of 30 OCD participants and 34 matched controls during an OCD-specific symptom provocation task. The study examined main effects and between-group differences in brain activation and glutamate levels during the task.A whole sample task-effects analysis on data meeting predefined quality criteria showed significant glutamate increases (n = 41 (22 OCD, 19 controls), mean change: 3.2 %, z = 3.75, p < .001) and task activation (n = 54 (26 OCD, 28 controls), p < .001) in the LOC during OCD blocks compared to neutral blocks. However, no differences in task-induced glutamate dynamics or activation between groups were found, nor a correlation between glutamate levels and task activation.We were able to measure task-induced increases in glutamate and BOLD levels, emphasizing its feasibility for OCD research. The absence of group differences highlights the need for further exploration to discern to what extent neurometabolite dynamics differ between OCD patients and controls. Once established, future studies can use pre-post intervention fMRS-fMRI to probe the effects of therapies modulating glutamate pathways in OCD.

Visual Impairment, Eye Conditions, and Diagnoses of Neurodegeneration and Dementia

Vision and eye conditions are associated with increased risk for Alzheimer disease and related dementias (ADRDs), but the nature of the association and the underlying biological pathways remain unclear. If causal, vision would be an important modifiable risk factor with viable population-level interventions. To evaluate potentially causal associations between visual acuity, eye conditions (specifically cataracts and myopia), neuroimaging outcomes, and ADRDs. A cohort and 2-sample bidirectional mendelian randomization (MR) study was conducted using UK Biobank participants and summary statistics from previously published genome-wide association studies on cataract, myopia, and AD. The participants included in the analysis were aged 55 to 70 years without dementia at baseline (calendar years 2006 to 2010), underwent genotyping, and reported on eye conditions; a subset completed visual acuity examinations (n = 69 852-71 429) or brain imaging (n = 36 591-36 855). Data were analyzed from August 15, 2022, through November 28, 2023. Self-reported cataracts, visual acuity, and myopia measured by refraction error. ADRD, AD, and vascular dementia were identified from electronic medical records. Total and regional brain volumes were determined using magnetic resonance imaging. The sample included 304 953 participants (mean [SD] age, 62.1 (4.1) years; 163 825 women [53.72%]); 14 295 (4.69%) had cataracts and 2754 (3.86%) had worse than 20/40 vision. Cataracts (hazard ratio [HR], 1.18; 95% CI, 1.07-1.29) and myopia (HR, 1.35; 95% CI, 1.06-1.70) were associated with a higher hazard of ADRD. In MR analyses to estimate potential causal effects, cataracts were associated with increased risk of vascular dementia (inverse variance-weighted odds ratio [OR], 1.92; 95% CI, 1.26-2.92) but were not associated with increased dementia (OR, 1.21; 95% CI, 0.98-1.50). There were no associations between myopia and dementia. In MR for potential reverse causality, AD was not associated with cataracts (inverse variance-weighted OR, 0.99; 95% CI, 0.96-1.01). Genetic risk for cataracts was associated with smaller total brain (β = -597.43 mm3; 95% CI, -1077.87 to -117.00 mm3) and gray matter (β = -375.17 mm3; 95% CI, -680.10 to -70.24 mm3) volumes, but not other brain regions. In this cohort and MR study of UK Biobank participants, cataracts were associated with increased risk of dementia, especially vascular dementia, and reduced total brain volumes. These findings lend further support to the hypothesis that cataract extraction may reduce the risk for dementia.

Low-cost, portable, easy-to-use kiosks to facilitate home-cage testing of nonhuman primates during vision-based behavioral tasks.

Nonhuman primates (NHPs), especially rhesus macaques, have significantly contributed to our understanding of the neural computations underlying human vision. Besides the established homologies in the visual brain areas between these species and our ability to probe detailed neural mechanisms in monkeys at multiple scales, NHPs' ability to perform human-like visual behavior makes them an extremely appealing animal model of human vision. Traditionally, such behavioral studies have been conducted in controlled laboratory settings, offering experimenters tight control over variables like luminance, eye movements, and auditory interference. However, in-lab experiments have several constraints, including limited experimental time, the need for dedicated human experimenters, additional lab space requirements, invasive surgeries for headpost implants, and extra time and training for chairing and head restraints. To overcome these limitations, we propose adopting home-cage behavioral training and testing of NHPs, enabling the administration of many vision-based behavioral tasks simultaneously across multiple monkeys with reduced human personnel requirements, no NHP head restraint, and monkeys' unrestricted access to experiments. In this article, we present a portable, low-cost, easy-to-use kiosk system developed to conduct home-cage vision-based behavioral tasks in NHPs. We provide details of its operation and build to enable more open-source development of this technology. Furthermore, we present validation results using behavioral measurements performed in the lab and in NHP home cages, demonstrating the system's reliability and potential to enhance the efficiency and flexibility of NHP behavioral research.NEW & NOTEWORTHY Training nonhuman primates (NHPs) for vision-based behavioral tasks in a laboratory setting is a time-consuming process and comes with many limitations. To overcome these challenges, we have developed an affordable, open-source, wireless, touchscreen training system that can be placed in the NHPs' housing environment. This system enables NHPs to work at their own pace. It provides a platform to implement continuous behavioral training protocols without major experimenter intervention and eliminates the need for other standard practices like NHP chair training, collar placement, and head restraints. Hence, these kiosks ultimately contribute to animal welfare and therefore better-quality neuroscience in the long run. In addition, NHPs quickly learn complex behavioral tasks using this system, making it a promising tool for wireless electrophysiological research in naturalistic, unrestricted environments to probe the relation between brain and behavior.

Factorized visual representations in the primate visual system and deep neural networks.

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether ('invariance'), represented in non-interfering subspaces of population activity ('factorization') or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters - lighting, background, camera viewpoint, and object pose - in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.

Familiarity enhances functional connectivity between visual and nonvisual regions of the brain during natural viewing.

We explored the neural correlates of familiarity with people and places using a naturalistic viewing paradigm. Neural responses were measured using functional magnetic resonance imaging, while participants viewed a movie taken from Game of Thrones. We compared inter-subject correlations and functional connectivity in participants who were either familiar or unfamiliar with the TV series. Higher inter-subject correlations were found between familiar participants in regions, beyond the visual brain, that are typically associated with the processing of semantic, episodic, and affective information. However, familiarity also increased functional connectivity between face and scene regions in the visual brain and the nonvisual regions of the familiarity network. To determine whether these regions play an important role in face recognition, we measured responses in participants with developmental prosopagnosia (DP). Consistent with a deficit in face recognition, the effect of familiarity was significantly attenuated across the familiarity network in DP. The effect of familiarity on functional connectivity between face regions and the familiarity network was also attenuated in DP. These results show that the neural response to familiarity involves an extended network of brain regions and that functional connectivity between visual and nonvisual regions of the brain plays an important role in the recognition of people and places during natural viewing.

Impaired contour object perception in psychosis.

Contour integration, the process of joining spatially separated elements into a single unified line, has consistently been found to be impaired in schizophrenia. Recent work suggests that this deficit could be associated with psychotic symptomatology, rather than a specific diagnosis such as schizophrenia. Examining a transdiagnostic sample of participants with psychotic psychopathology, we obtained quantitative indices of contour perception in a psychophysical behavioral task. We found impaired contour discrimination performance among people with psychotic psychopathology (PwPP, n = 62) compared to healthy controls (n = 34) and biological relatives of PwPP (n = 44). Participants with schizophrenia (n = 31) showed impaired task performance compared to participants with bipolar disorder (n = 18). We also measured responses during an analogous task using ultra-high field (7T) functional MRI and found higher responses in the lateral occipital cortex of PwPP compared to controls. Using task-based functional connectivity analyses, we observed abnormal connectivity between visual brain areas during contour perception among PwPP. These connectivity differences only emerged when participants had to distinguish the contour object from background distractors, suggesting that a failure to suppress noise elements relative to contour elements may underlie impaired contour processing in PwPP. Our results are consistent with impaired contour integration in psychotic psychopathology, and especially schizophrenia, that is related to cognitive dysfunction, and may be linked to impaired functional connectivity across visual regions.

Comparative Brain Morphology of Cleaning and Sponge-Dwelling Elacatinus Gobies.

Comparative studies of brain anatomy between closely related species have been very useful in demonstrating selective changes in brain structure. Within-species comparisons can be particularly useful for identifying changes in brain structure caused by contrasting environmental selection pressures. Here, we aimed to understand whether differences within and between species in habitat use and foraging behaviour influence brain morphology, on both ecological and evolutionary time scales. We used as a study model three species of the Elacatinus genus that differ in their habitat-foraging mode. The obligatory cleaning goby Elacatinus evelynae inhabits mainly corals and feeds mostly on ectoparasites removed from larger fish during cleaning interactions. In contrast, the obligatory sponge-dwelling goby Elacatinus chancei inhabits tubular sponges and feeds on microinvertebrates buried in the sponges' tissues. Finally, in the facultatively cleaning goby Elacatinus prochilos, individuals can adopt either phenotype, the cleaning or the sponge-dwelling habitat-foraging mode. By comparing the brains of the facultative goby phenotypes to the brains of the obligatory species we can test whether brain morphology is better predicted by phylogenetic relatedness or the habitat-foraging modes (cleaning × sponge dwelling). We found that E. prochilos brains from both types (cleaning and sponge dwelling) were highly similar to each other. Their brains were in general more similar to the brains of the most closely related species, E. evelynae (obligatory cleaning species), than to the brains of E. chancei (sponge-dwelling species). In contrast, we found significant brain structure differences between the cleaning species (E. evelynae and E. prochilos) and the sponge-dwelling species (E. chancei). These differences revealed independent changes in functionally correlated brain areas that might be ecologically adaptive. E. evelynae and E. prochilos had a relatively larger visual input processing brain axis and a relatively smaller lateral line input processing brain axis than E. chancei. The similar brain morphology of the two types of E. prochilos corroborates other studies showing that individuals of both types can be highly plastic in their social and foraging behaviours. Our results in the Elacatinus species suggest that morphological adaptations of the brain are likely to be found in specialists whereas species that are more flexible in their habitat may only show behavioural plasticity without showing anatomical differences.

Decoding visual brain representations from electroencephalography through knowledge distillation and latent diffusion models

Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain–computer interfaces. Our study presents an innovative method that employs knowledge distillation to train an EEG classifier and reconstruct images from the ImageNet and THINGS-EEG 2 datasets using only electroencephalography (EEG) data from participants who have viewed the images themselves (i.e. “brain decoding”). We analyzed EEG recordings from 6 participants for the ImageNet dataset and 10 for the THINGS-EEG 2 dataset, exposed to images spanning unique semantic categories. These EEG readings were converted into spectrograms, which were then used to train a convolutional neural network (CNN), integrated with a knowledge distillation procedure based on a pre-trained Contrastive Language-Image Pre-Training (CLIP)-based image classification teacher network. This strategy allowed our model to attain a top-5 accuracy of 87%, significantly outperforming a standard CNN and various RNN-based benchmarks. Additionally, we incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images that had elicited EEG activity. Therefore, our architecture not only decodes images from neural activity but also offers a credible image reconstruction from EEG only, paving the way for, e.g., swift, individualized feedback experiments.