Levels-of-processing effects in first-degree relatives of individuals with schizophrenia.

BACKGROUND: First-degree relatives of individuals with schizophrenia show cognitive impairments that are similar to but less severe than their ill relatives. We have shown that memory impairments can be improved and prefrontal cortical (PFC) activity increased in individuals with schizophrenia by providing beneficial encoding strategies. The current study used a similar paradigm to determine whether siblings of individuals with schizophrenia (SIBs) also show increases in brain activity when presented with beneficial encoding strategies. METHODS: Twenty-one SIBs and 38 siblings of healthy comparison subjects underwent functional magnetic resonance imaging scans while engaged in deep (abstract/concrete judgments) and shallow (orthographic judgments) encoding. Subjects were then given a recognition memory test. RESULTS: The groups did not differ on encoding or recognition accuracy, and the SIBs benefited from deep encoding to a similar degree as control subjects. The SIBs showed deep encoding-related activity in a number of PFC regions typically activated during semantic processing. However, SIBs showed more activity than control subjects in three subregions of PFC (left BA 44 & BA 47 bilaterally). CONCLUSIONS: Siblings of individuals with schizophrenia benefit from supportive verbal encoding conditions. Like individuals with schizophrenia, SIBs also show increased task-related activity in a larger number of PFC subregions than control subjects during deep verbal encoding.     

From the Cover: Convolutional networks for fast,energy-efficient neuromorphic computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.The human brain is capable of remarkable acts of perception while consuming very little energy. The dream of brain-inspired computing is to build machines that do the same, requiring high-accuracy algorithms and efficient hardware to run those algorithms. On the algorithm front, building on classic work on backpropagation (1), the neocognitron (2), and convolutional networks (3), deep learning has made great strides in achieving human-level performance on a wide range of recognition tasks (4). On the hardware front, building on foundational work on silicon neural systems (5), neuromorphic computing, using novel architectural primitives, has recently demonstrated hardware capable of running 1 million neurons and 256 million synapses for extremely low power (just 70 mW at real-time operation) (6). Bringing these approaches together holds the promise of a new generation of embedded, real-time systems, but first requires reconciling key differences in the structure and operation between contemporary algorithms and hardware. Here, we introduce and demonstrate an approach we call Eedn, energy-efficient deep neuromorphic networks, which creates convolutional networks whose connections, neurons, and weights have been adapted to run inference tasks on neuromorphic hardware.For structure, typical convolutional networks place no constraints on filter sizes, whereas neuromorphic systems can take advantage of blockwise connectivity that limits filter sizes, thereby saving energy because weights can now be stored in local on-chip memory within dedicated neural cores. Here, we present a convolutional network structure that naturally maps to the efficient connection primitives used in contemporary neuromorphic systems. We enforce this connectivity constraint by partitioning filters into multiple groups and yet maintain network integration by interspersing layers whose filter support region is able to cover incoming features from many groups by using a small topographic size (7).For operation, contemporary convolutional networks typically use high precision ( ≥ 32-bit) neurons and synapses to provide continuous derivatives and support small incremental changes to network state, both formally required for backpropagation-based gradient learning. In comparison, neuromorphic designs can use one-bit spikes to provide event-based computation and communication (consuming energy only when necessary) and can use low-precision synapses to colocate memory with computation (keeping data movement local and avoiding off-chip memory bottlenecks). Here, we demonstrate that by introducing two constraints into the learning rule—binary-valued neurons with approximate derivatives and trinary-valued ({−1,0,1}) synapses—it is possible to adapt backpropagation to create networks directly implementable using energy efficient neuromorphic dynamics. This approach draws inspiration from the spiking neurons and low-precision synapses of the brain (8) and builds on work showing that deep learning can create networks with constrained connectivity (9), low-precision synapses (10, 11), low-precision neurons (12–14), or both low-precision synapses and neurons (15, 16). For input data, we use a first layer to transform multivalued, multichannel input into binary channels using convolution filters that are learned via backpropagation (12, 16) and whose output can be sent on chip in the form of spikes. These binary channels, intuitively akin to independent components (17) learned with supervision, provide a parallel distributed representation to carry out high-fidelity computation without the need for high-precision representation.Critically, we demonstrate that bringing the above innovations together allows us to create networks that approach state-of-the-art accuracy performing inference on eight standard datasets, running on a neuromorphic chip at between 1,200 and 2,600 frames/s (FPS), using between 25 and 275 mW. We further explore how our approach scales by simulating multichip configurations. Ease-of-use is achieved using training tools built from existing, optimized deep learning frameworks (18), with learned parameters mapped to hardware using a high-level deployment language (19). Although we choose the IBM TrueNorth chip (6) for our example deployment platform, the essence of our constructions can apply to other emerging neuromorphic approaches (20–23) and may lead to new architectures that incorporate deep learning and efficient hardware primitives from the ground up.     

Cognitive Subtyping in Schizophrenia: A Latent Profile Analysis

Cognitive dysfunction is a core feature of schizophrenia. The subtyping of cognitive performance in schizophrenia may aid the refinement of disease heterogeneity. The literature on cognitive subtyping in schizophrenia, however, is limited by variable methodologies and neuropsychological tasks, lack of validation, and paucity of studies examining longitudinal stability of profiles. It is also unclear if cognitive profiles represent a single linear severity continuum or unique cognitive subtypes. Cognitive performance measured with the Brief Assessment of Cognition in Schizophrenia was analyzed in schizophrenia patients (n = 767). Healthy controls (n = 1012) were included as reference group. Latent profile analysis was performed in a schizophrenia discovery cohort (n = 659) and replicated in an independent cohort (n = 108). Longitudinal stability of cognitive profiles was evaluated with latent transition analysis in a 10-week follow-up cohort. Confirmatory factor analysis (CFA) was carried out to investigate if cognitive profiles represent a unidimensional structure. A 4-profile solution was obtained from the discovery cohort and replicated in an independent cohort. It comprised of a “less-impaired” cognitive subtype, 2 subtypes with “intermediate cognitive impairment” differentiated by executive function performance, and a “globally impaired” cognitive subtype. This solution showed relative stability across time. CFA revealed that cognitive profiles are better explained by distinct meaningful profiles than a severity linear continuum. Associations between profiles and negative symptoms were observed. The subtyping of schizophrenia patients based on cognitive performance and its associations with symptomatology may aid phenotype refinement, mapping of specific biological mechanisms, and tailored clinical treatments.     

Structure–function relationship of working memory activity with hippocampal and prefrontal cortex volumes

A rapidly increasing number of studies are quantifying the system-level network architecture of the human brain based on structural-to-structural and functional-to-functional relationships. However, a largely unexplored area is the nature and existence of “cross-modal” structural–functional relationships, in which, for example, the volume (or other morphological property) of one brain region is related to the functional response to a given task either in that same brain region, or another brain region. The present study investigated whether the gray matter volume of a selected group of structures (superior, middle, and inferior frontal gyri, thalamus, and hippocampus) was correlated with the fMRI response to a working memory task, within a mask of regions previously identified as involved with working memory. The subjects included individuals with schizophrenia, their siblings, and healthy controls (n = 154 total). Using rigorous permutation testing to define the null distribution, we found that the volume of the superior and middle frontal gyri was correlated with working memory activity within clusters in the intraparietal sulcus (i.e., dorsal parietal cortex) and that the volume of the hippocampus was correlated with working memory activity within clusters in the dorsal anterior cingulate cortex and left inferior frontal gyrus. However, we did not find evidence that the identified structure–function relationships differed between subject groups. These results show that long-distance structural–functional relationships exist within the human brain. The study of such cross-modal relationships represents an additional approach for studying systems-level interregional brain networks.