Computational modeling of pair-association memory in inferior temporal cortex

Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition

Our brains continuously acquire sensory information and make judgments even when visual information is limited. In some circumstances, an ambiguous object can be recognized from how it moves, such as an animal hopping or a plane flying overhead. Yet it remains unclear how movement is processed by brain areas involved in visual object recognition. Here we investigate whether inferior temporal (IT) cortex, an area known for its relevance in visual form processing, has access to motion information during recognition. We developed a matching task that required monkeys to recognize moving shapes with variable levels of shape degradation. Neural recordings in area IT showed that, surprisingly, some IT neurons responded stronger to degraded shapes than clear ones. Furthermore, neurons exhibited motion sensitivity at different times during the presentation of the blurry target. Population decoding analyses showed that motion patterns could be decoded from IT neuron pseudo-populations. Contrary to previous findings, these results suggest that neurons in IT can integrate visual motion and shape information, particularly when shape information is degraded, in a way that has been previously overlooked. Our results highlight the importance of using challenging multifeature recognition tasks to understand the role of area IT in naturalistic visual object recognition.

Abnormal functional connectivity strength in first-episode, drug-naïve adult patients with major depressive disorder

BACKGROUND

The pathogenesis of major depressive disorder (MDD) is complicated and equivocal. Previous studies have found an incidence of abnormal changes of neural networks, with plentiful evidence pointing the finger of suspicion firmly at the default mode network (DMN) and cortico-limbic networks. The aim of the present study was to use the approach of functional connectivity strength (FCS) to directly investigate the features of spontaneous brain activity in the case of first-episode, drug-naïve adult patients with MDD at rest.

METHODS

Resting-state functional magnetic resonance imaging (MRI) scans were performed on 23 first-episode drug-naïve major depressive disorder (MDD) patients and 20 healthy controls (HCs). In this study, using graph-theory approaches(FCS), we computed the characteristics of brain connectivity. Simultaneously, we used a series of validated test procedures to evaluate the patients' cognitive function. Subsequently, the results were compared with the peak of FCS value and a correlation analysis was conducted.

RESULTS

Compared with the HCs group, MDD patients showed significantly decreased FCS in bilateral posterior cingulate cortex (PCC)/precuneus and bilateral prefrontal cortex(PFC) and increased FCS in right posterior central gyrus, left thalamus and left temporal lobe. These brain regions belongs to the default-mode network and cortico-limbic networks. Finally, the correlation analyses showed the negative correlation of the FCS values in the left posterior cingulate cortex (PCC)/precuneus and Hamilton Anxiety Rating Scale (HAMA, r = -0.472, p = .023), Stroop Color Word Test-A(SCWT-A, r = -0.451, p = .031), Stroop Color Word Test-B(SCWT-B, r = -0.588, p = .003).Meanwhile, there was negative correlation between the FCS values in the left thalamus and SCWT-A(r = -0.473, p = .023), SCWT-B(r = -0.465, p = .025), SCWTC(r = -0.524, p = .010).In addition, the FCS values in the right PCC has negative correlation with Montgomery Asberg Depression Rating Scale (MADRS) (r = -0.433, p = .039).

CONCLUSIONS

DMN is an important node of MDD. FCS within the default mode network and cortico-limbic networks in patients with major depressive disorder has been changed in the early stage of MDD. FCS can provide favourable and additional evidence in the investigation of brain pathophysiology and therapy in depression.

Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging

Magnetic resonance imaging studies have reported significant functional and structural differences between depressed patients and controls. Little attention has been given, however, to the abnormalities in anatomical connectivity in depressed patients. In the present study, we aim to investigate the alterations in connectivity of whole-brain anatomical networks in those suffering from major depression by using machine learning approaches. Brain anatomical networks were extracted from diffusion magnetic resonance images obtained from both 22 first-episode, treatment-naive adults with major depressive disorder and 26 matched healthy controls. Using machine learning approaches, we differentiated depressed patients from healthy controls based on their whole-brain anatomical connectivity patterns and identified the most discriminating features that represent between-group differences. Classification results showed that 91.7% (patients=86.4%, controls=96.2%; permutation test, p<0.0001) of subjects were correctly classified via leave-one-out cross-validation. Moreover, the strengths of all the most discriminating connections were increased in depressed patients relative to the controls, and these connections were primarily located within the cortical-limbic network, especially the frontal-limbic network. These results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease.

Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis

Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Multivariate pattern analysis was employed to classify 24 depressed patients from 29 demographically matched healthy volunteers. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% (P < 0.0001) of subjects were correctly classified by leave-one-out cross-validation, including 100% identification of all patients. The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression. Moreover, the amygdala, anterior cingulate cortex, parahippocampal gyrus and hippocampus, which exhibit high discriminative power in classification, may play important roles in the pathophysiology of this disorder. The current study may shed new light on the pathological mechanism of major depression and suggests that whole-brain resting-state functional connectivity magnetic resonance imaging may provide potential effective biomarkers for its clinical diagnosis.

A computational model of Basal Ganglia and its role in memory retrieval in rewarded visual memory tasks

Visual working memory (WM) tasks involve a network of cortical areas such as inferotemporal, medial temporal and prefrontal cortices. We suggest here to investigate the role of the basal ganglia (BG) in the learning of delayed rewarded tasks through the selective gating of thalamocortical loops. We designed a computational model of the visual loop linking the perirhinal cortex, the BG and the thalamus, biased by sustained representations in prefrontal cortex. This model learns concurrently different delayed rewarded tasks that require to maintain a visual cue and to associate it to itself or to another visual object to obtain reward. The retrieval of visual information is achieved through thalamic stimulation of the perirhinal cortex. The input structure of the BG, the striatum, learns to represent visual information based on its association to reward, while the output structure, the substantia nigra pars reticulata, learns to link striatal representations to the disinhibition of the correct thalamocortical loop. In parallel, a dopaminergic cell learns to associate striatal representations to reward and modulates learning of connections within the BG. The model provides testable predictions about the behavior of several areas during such tasks, while providing a new functional organization of learning within the BG, putting emphasis on the learning of the striatonigral connections as well as the lateral connections within the substantia nigra pars reticulata. It suggests that the learning of visual WM tasks is achieved rapidly in the BG and used as a teacher for feedback connections from prefrontal cortex to posterior cortices.

Semantic associations between signs and numerical categories in the prefrontal cortex

The utilization of symbols such as words and numbers as mental tools endows humans with unrivalled cognitive flexibility. In the number domain, a fundamental first step for the acquisition of numerical symbols is the semantic association of signs with cardinalities. We explored the primitives of such a semantic mapping process by recording single-cell activity in the monkey prefrontal and parietal cortices, brain structures critically involved in numerical cognition. Monkeys were trained to associate visual shapes with varying numbers of items in a matching task. After this long-term learning process, we found that the responses of many prefrontal neurons to the visual shapes reflected the associated numerical value in a behaviorally relevant way. In contrast, such association neurons were rarely found in the parietal lobe. These findings suggest a cardinal role of the prefrontal cortex in establishing semantic associations between signs and abstract categories, a cognitive precursor that may ultimately give rise to symbolic thinking in linguistic humans.

A model for integrating elementary neural functions into delayed-response behavior

It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

Before we do anything, our brain must construct neural representations of the operations required. Imaging and recording techniques are indeed providing ever more detailed insight into how different regions of the brain contribute to behavior. However, it has remained elusive exactly how these various regions then come to cooperate with each other, thus organizing the brain-scale activity patterns needed for even the simplest planned tasks. In the present work, the authors propose a neural network model built around the hypothesis of a modular organization of brain activity, where relatively autonomous basic neural functions useful at a given moment are recruited and integrated into actual behavior. At the heart of the model are regulating structures that restrain information from flowing freely between the different cortical areas involved, releasing it instead in a controlled fashion able to produce the appropriate response. The dynamics of the network, simulated on a computer, enables it to pass simple cognitive tests while reproducing data gathered on primates carrying out these same tasks. This suggests that the model might constitute an appropriate framework for studying the neural basis of more general behavior.

Evidence for perceptual "trapping" and adaptation in multistable binocular rivalry

When a different pattern is presented to each eye, the perceived image spontaneously alternates between the two patterns (binocular rivalry); the dynamics of these bistable alternations are known to be stochastic. Examining multistable binocular rivalry (involving four dominant percepts), we demonstrated path dependence and on-line adaptation, which were equivalent whether perceived patterns were formed by single-eye dominance or by mixed-eye dominance. The spontaneous perceptual transitions tended to get trapped within a pair of related global patterns (e.g., opponent shapes and symmetric patterns), and during such trapping, the probability of returning to the repeatedly experienced patterns gradually decreased (postselection pattern adaptation). These results suggest that the structure of global shape coding and its adaptation play a critical role in directing spontaneous alternations of visual awareness in perceptual multistability.