The neurons that mistook a hat for a face

Despite evidence that context promotes the visual recognition of objects, decades of research have led to the pervasive notion that the object processing pathway in primate cortex consists of multiple areas that each process the intrinsic features of a few particular categories (e.g. faces, bodies, hands, objects, and scenes). Here we report that such category-selective neurons do not in fact code individual categories in isolation but are also sensitive to object relationships that reflect statistical regularities of the experienced environment. We show by direct neuronal recording that face-selective neurons respond not just to an image of a face, but also to parts of an image where contextual cues-for example a body-indicate a face ought to be, even if what is there is not a face

How to Create and Use Binocular Rivalry

Each of our eyes normally sees a slightly different image of the world around us. The brain can combine these two images into a single coherent representation. However, when the eyes are presented with images that are sufficiently different from each other, an interesting thing happens: Rather than fusing the two images into a combined conscious percept, what transpires is a pattern of perceptual alternations where one image dominates awareness while the other is suppressed; dominance alternates between the two images, typically every few seconds. This perceptual phenomenon is known as binocular rivalry. Binocular rivalry is considered useful for studying perceptual selection and awareness in both human and animal models, because unchanging visual input to each eye leads to alternations in visual awareness and perception. To create a binocular rivalry stimulus, all that is necessary is to present each eye with a different image at the same perceived location. There are several ways of doing this, but newcomers to the field are often unsure which method would best suit their specific needs. The purpose of this article is to describe a number of inexpensive and straightforward ways to create and use binocular rivalry. We detail methods that do not require expensive specialized equipment and describe each method's advantages and disadvantages. The methods described include the use of red-blue goggles, mirror stereoscopes and prism goggles

Process evaluation of a treatment program for mood and anxiety disorders among emerging adults: Preentry factors, engagement, and outcomes

Objective: Effective mental health services for emerging adults are needed. This work evaluated the logic model of one such program and assessed participation and medium-term outcomes. Methods: Baseline data were collected from 398 emerging adults attending an intake appointment at a mood and anxiety disorders treatment program in Canada for persons ages 16-25. Questionnaires about demographic characteristics, prior help seeking, symptoms, functional impairment, and health satisfaction were completed at baseline and at follow-up, approximately 2 to 10 months later (mean=6 months), depending on participants\u27 availability and willingness. Program satisfaction was also assessed. Preentry characteristics and disengagement were evaluated. Repeated-measures analyses were used to evaluate outcomes. Results: The program did not require physician referral; however, emerging adults who contacted the program had extensive prior help seeking: 73% had seen a family doctor and 32% had visited an emergency department. Among 370 individuals for whom full intake data were available, scores indicated moderate depression, moderate anxiety, and low satisfaction with quality of health. They reported either not functioning or underfunctioning for a mean of 4.3 days per week. Follow-up data indicated significant improvement on all measures, including clinically significant improvement in both depression and functioning. Patient satisfaction was high, and quality of health improved significantly. Conclusions: Results indicate that the model studied, which emphasizes early-stage intervention for mood and anxiety disorders among emerging adults, was associated with statistical and clinical improvement at intermediate follow-up. Outputs and medium-term outcomes of the model were satisfied

Electrophysiological Low-Frequency Coherence and Cross-Frequency Coupling Contribute to BOLD Connectivity

SummaryBrain networks are commonly defined using correlations between blood oxygen level-dependent (BOLD) signals in different brain areas. Although evidence suggests that gamma-band (30–100 Hz) neural activity contributes to local BOLD signals, the neural basis of interareal BOLD correlations is unclear. We first defined a visual network in monkeys based on converging evidence from interareal BOLD correlations during a fixation task, task-free state, and anesthesia, and then simultaneously recorded local field potentials (LFPs) from the same four network areas in the task-free state. Low-frequency oscillations (<20 Hz), and not gamma activity, predominantly contributed to interareal BOLD correlations. The low-frequency oscillations also influenced local processing by modulating gamma activity within individual areas. We suggest that such cross-frequency coupling links local BOLD signals to BOLD correlations across distributed networks

Brain Transfer: Spectral Analysis of Cortical Surfaces and Functional Maps

International audienceThe study of brain functions using fMRI often requires an accuratematching of cortical surface data for comparing brain activation acrossa population. In this context, several tasks are critical, such as surface in-flation for cortical visualizations and measurements, surface matching andalignment of functional data for group-level analyses. Present methods typicallytreat each task separately and can be computationally expensive. It takesfor example several hours to smooth and match a single pair of cortical surfaces.Furthermore, conventional methods rely on anatomical features to drivethe alignment of functional data across individuals, whereas their relation tofunction can vary across a population. To address these issues, we proposeBrain Transfer, a spectral framework that unifies cortical smoothing, pointmatching with confidence regions, and transfer of functional maps, all withinminutes of computation. Spectral methods have the advantage of decomposingshapes into intrinsic geometrical harmonics, but suffer from the inherentinstability of these harmonics. This limits their direct comparison in surfacematching, and prevents the spectral transfer of functions. Our contributionsconsist of, first, the optimization of a spectral transformation matrix, whichcombines both, point correspondence and change of eigenbasis, and second,a localized spectral decomposition of functional data, via focused harmonics.Brain Transfer enables the transfer of surface functions across interchangeablecortical spaces, accounts for localized confidence, and gives a new way toperform statistics on surfaces. We illustrate the benefits of spectral transfersby exploring the shape and functional variability of retinotopy, which remainschallenging with conventional methods. We find a higher degree of accuracyin the alignment of retinotopy, exceeding those of conventional methods

Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex

One of the most impactful findings in computational neuroscience over the past decade is that the object recognition accuracy of deep neural networks (DNNs) correlates with their ability to predict neural responses to natural images in the inferotemporal (IT) cortex. This discovery supported the long-held theory that object recognition is a core objective of the visual cortex, and suggested that more accurate DNNs would serve as better models of IT neuron responses to images. Since then, deep learning has undergone a revolution of scale: billion parameter-scale DNNs trained on billions of images are rivaling or outperforming humans at visual tasks including object recognition. Have today's DNNs become more accurate at predicting IT neuron responses to images as they have grown more accurate at object recognition? Surprisingly, across three independent experiments, we find this is not the case. DNNs have become progressively worse models of IT as their accuracy has increased on ImageNet. To understand why DNNs experience this trade-off and evaluate if they are still an appropriate paradigm for modeling the visual system, we turn to recordings of IT that capture spatially resolved maps of neuronal activity elicited by natural images. These neuronal activity maps reveal that DNNs trained on ImageNet learn to rely on different visual features than those encoded by IT and that this problem worsens as their accuracy increases. We successfully resolved this issue with the neural harmonizer, a plug-and-play training routine for DNNs that aligns their learned representations with humans. Our results suggest that harmonized DNNs break the trade-off between ImageNet accuracy and neural prediction accuracy that assails current DNNs and offer a path to more accurate models of biological vision

Patient-specific detection of cerebral blood flow alterations as assessed by arterial spin labeling in drug-resistant epileptic patients

Electrophysiological and hemodynamic data can be integrated to accurately and precisely identify the generators of abnormal electrical activity in drug-resistant focal epilepsy. Arterial Spin Labeling (ASL), a magnetic resonance imaging (MRI) technique for quantitative noninvasive measurement of cerebral blood flow (CBF), can provide a direct measure of variations in cerebral perfusion associated with the epileptic focus. In this study, we aimed to confirm the ASL diagnostic value in the identification of the epileptogenic zone, as compared to electrical source imaging (ESI) results, and to apply a template-based approach to depict statistically significant CBF alterations. Standard video-electroencephalography (EEG), high-density EEG, and ASL were performed to identify clinical seizure semiology and noninvasively localize the epileptic focus in 12 drug-resistant focal epilepsy patients. The same ASL protocol was applied to a control group of 17 healthy volunteers from which a normal perfusion template was constructed using a mixed-effect approach. CBF maps of each patient were then statistically compared to the reference template to identify perfusion alterations. Significant hypo- and hyperperfused areas were identified in all cases, showing good agreement between ASL and ESI results. Interictal hypoperfusion was observed at the site of the seizure in 10/12 patients and early postictal hyperperfusion in 2/12. The epileptic focus was correctly identified within the surgical resection margins in the 5 patients who underwent lobectomy, all of which had good postsurgical outcomes. The combined use of ESI and ASL can aid in the noninvasive evaluation of drug-resistant epileptic patients

Republication: Targeting PI3KC2β Impairs Proliferation and Survival in Acute Leukemia, Brain Tumours and Neuroendocrine Tumours

BACKGROUND Eight human catalytic phosphoinositide 3-kinase (PI3K) isoforms exist which are subdivided into three classes. While class I isoforms have been well-studied in cancer, little is known about the functions of class II PI3Ks. MATERIALS AND METHODS The expression pattern and functions of the class II PI3KC2β isoform were investigated in a panel of tumour samples and cell lines. RESULTS Overexpression of PI3KC2β was found in subsets of tumours and cell lines from acute myeloid leukemia (AML), glioblastoma multiforme (GBM), medulloblastoma (MB), neuroblastoma (NB), and small cell lung cancer (SCLC). Specific pharmacological inhibitors of PI3KC2β or RNA interference impaired proliferation of a panel of human cancer cell lines and primary cultures. Inhibition of PI3KC2β also induced apoptosis and sensitised the cancer cells to chemotherapeutic agents. CONCLUSION Together, these data show that PI3KC2β contributes to proliferation and survival in AML, brain tumours and neuroendocrine tumours, and may represent a novel target in these malignancies