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Abstract This is a report on a 37-patient continuation study of the open ended, Omega-3 Fatty Acid (O-3FA) add-on study. Subjects consisted of the original 19 patients, along with 18 new patients recruited and followed in the same fashion as the first nineteen. Subjects carried a DSM-IV-TR diagnosis of Bipolar Disorder and were visiting a Mood Disorder Clinic regularly through the length of the study. At each visit, patients' clinical status was monitored using the Clinical Monitoring Form. Subjects reported on the frequency and severity of irritability experienced during the preceding ten days; frequency was measured by way of percentage of days in which subjects experienced irritability, while severity of that irritability was rated on a Likert scale of 1 – 4 (if present). The irritability component of Young Mania Rating Scale (YMRS) was also recorded quarterly on 13 of the 39 patients consistently. Patients had persistent irritability despite their ongoing pharmacologic and psychotherapy. Omega-3 Fatty Acid intake helped with the irritability component of patients suffering from bipolar disorder with a significant presenting sign of irritability. Low dose (1 to 2 grams per day), add-on O-3FA may also help with the irritability component of different clinical conditions, such as schizophrenia, borderline personality disorder and other psychiatric conditions with a common presenting sign of irritability.

Many experiences unfold predictably over time. Memory for these temporal regularities enables anticipation of events multiple steps into the future. Because temporally predictable events repeat over days, weeks, and years, we must maintain-and potentially transform-memories of temporal structure to support adaptive behavior. We explored how individuals build durable models of temporal regularities to guide multistep anticipation. Healthy young adults (Experiment 1: N = 99, age range = 18-40 years; Experiment 2: N = 204, age range = 19-40 years) learned sequences of scene images that were predictable at the category level and contained incidental perceptual details. Individuals then anticipated upcoming scene categories multiple steps into the future, immediately and at a delay. Consolidation increased the efficiency of anticipation, particularly for events further in the future, but diminished access to perceptual features. Further, maintaining a link-based model of the sequence after consolidation improved anticipation accuracy. Consolidation may therefore promote efficient and durable models of temporal structure, thus facilitating anticipation of future events.

Understanding and remembering the complex experiences of everyday life relies critically on prior schematic knowledge about how events in our world unfold over time. How does the brain construct event representations from a library of schematic scripts, and how does activating a specific script impact the way that events are segmented in time? We developed a novel set of 16 audio narratives, each of which combines one of four location-relevant event scripts (restaurant, airport, grocery store, and lecture hall) with one of four socially relevant event scripts (breakup, proposal, business deal, and meet cute), and presented them to participants in an fMRI study and a separate online study. Responses in the angular gyrus, parahippocampal gyrus, and subregions of the medial prefrontal cortex (mPFC) were driven by scripts related to both location and social information, showing that these regions can track schematic sequences from multiple domains. For some stories, participants were primed to attend to one of the two scripts by training them to listen for and remember specific script-relevant episodic details. Activating a location-related event script shifted the timing of subjective event boundaries to align with script-relevant changes in the narratives, and this behavioral shift was mirrored in the timing of neural responses, with mPFC event boundaries (identified using a hidden Markov model) aligning to location-relevant rather than socially relevant boundaries when participants were location primed. Our findings demonstrate that neural event dynamics are actively modulated by top-down goals and provide new insight into how narrative event representations are constructed through the activation of temporally structured prior knowledge., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Memory for temporal structure enables both planning of future events and retrospection of past events. We investigated how the brain flexibly represents extended temporal sequences into the past and future during anticipation. Participants learned sequences of environments in immersive virtual reality. Pairs of sequences had the same environments in a different order, enabling context-specific learning. During fMRI, participants anticipated upcoming environments multiple steps into the future in a given sequence. Temporal structure was represented in the hippocampus and across higher-order visual regions (1) bidirectionally, with graded representations into the past and future and (2) hierarchically, with further events into the past and future represented in successively more anterior brain regions. In hippocampus, these bidirectional representations were context-specific, and suppression of far-away environments predicted response time costs in anticipation. Together, this work sheds light on how we flexibly represent sequential structure to enable planning over multiple timescales.

Narrative stimuli offer a unique opportunity for research in cognitive neuroscience because they evoke cognitive processes that are difficult or impossible to study with traditional paradigms. An especially compelling feature of narratives is their temporal structure, which allows for meaningful predictions about upcoming events. As we proceed through a narrative, we can maintain a complex set of short- and long-term guesses about the future and continually refine our predictions as the story unfolds. Experiments using narratives can allow researchers to probe the ways in which memory systems are flexibly used during perception, including the mechanisms by which continuous experiences are segmented into discrete events. Despite the challenges of using narratives and other naturalistic stimuli in experimental research, these approaches offer a new window into critical components of real-world cognition., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

When forming a memory of an experience that is unfolding over time, we can use our schematic knowledge about the world (constructed based on many prior episodes) to predict what will transpire. We developed a novel paradigm to study how the development of a complex schema influences predictive processes during perception and impacts sequential memory. Participants learned to play a novel board game ('four-in-a-row') across six training sessions and repeatedly performed a memory test in which they watched and recalled sequences of moves from the game. We found that participants gradually became better at remembering sequences from the game as their schema developed, driven by improved accuracy for schema-consistent moves. Eye tracking revealed that increased predictive eye movements during encoding, which were most prevalent in expert players, were associated with better memory. Our results identify prediction as a mechanism by which schematic knowledge can improve episodic memory., Competing Interests: JH, IV, WM, CB No competing interests declared, (© 2023, Huang et al.)

Here we introduce a new python package, img2fmri, to predict group-level fMRI responses to individual images. This prediction model uses an artificial deep neural network (DNN), as DNNs have been successful at predicting cortical responses in the human visual cortex when trained on real world visual categorization tasks. To validate our model, we predict fMRI responses to images our model has not previously seen from a new dataset. We then show how our frame-by-frame prediction model can be extended to a continuous visual stimulus by predicting an fMRI response to Pixar Animation Studio's short film Partly Cloudy . In analyzing the timepoint-timepoint similarity of our predicted fMRI response around human-annotated event boundaries in the movie, we find that our model outperforms the baseline model in describing the dynamics of the real fMRI response around these event boundaries, particularly in the timepoints just before and at an event. These analyses suggest that in visual areas of the brain, at least some of the temporal dynamics we see in the brain's processing of continuous, naturalistic stimuli can be explained by dynamics in the stimulus itself, since they can be predicted from our frame-by-frame model. All code, analyses, tutorials, and installation instructions can be found at https://github.com/dpmlab/img2fmri.

How infants experience the world is fundamental to understanding their cognition and development. A key principle of adult experience is that, despite receiving continuous sensory input, we perceive this input as discrete events. Here we investigate such event segmentation in infants and how it differs from adults. Research on event cognition in infants often uses simplified tasks in which (adult) experimenters help solve the segmentation problem for infants by defining event boundaries or presenting discrete actions/vignettes. This presupposes which events are experienced by infants and leaves open questions about the principles governing infant segmentation. We take a different, data-driven approach by studying infant event segmentation of continuous input. We collected whole-brain functional MRI (fMRI) data from awake infants (and adults, for comparison) watching a cartoon and used a hidden Markov model to identify event states in the brain. We quantified the existence, timescale, and organization of multiple-event representations across brain regions. The adult brain exhibited a known hierarchical gradient of event timescales, from shorter events in early visual regions to longer events in later visual and associative regions. In contrast, the infant brain represented only longer events, even in early visual regions, with no timescale hierarchy. The boundaries defining these infant events only partially overlapped with boundaries defined from adult brain activity and behavioral judgments. These findings suggest that events are organized differently in infants, with longer timescales and more stable neural patterns, even in sensory regions. This may indicate greater temporal integration and reduced temporal precision during dynamic, naturalistic perception.

How does the representation of naturalistic life events change with age? Here, we analyzed fMRI data from 414 children and adolescents (5-19 years) as they watched a narrative movie. In addition to changes in the degree of inter-subject correlation (ISC) with age in sensory and medial parietal regions, we used a novel measure (between-group ISC) to reveal age-related shifts in the responses across the majority of the neocortex. Over the course of development, brain responses became more discretized into stable and coherent events and shifted earlier in time to anticipate upcoming perceived event transitions, measured behaviorally in an age-matched sample. However, hippocampal responses to event boundaries actually decreased with age, suggesting a shifting division of labor between episodic encoding processes and schematic event representations between the ages of 5 and 19., Competing Interests: SC, NT, CB No competing interests declared, (© 2022, Cohen et al.)

Schematic prior knowledge can scaffold the construction of event memories during perception and also provide structured cues to guide memory search during retrieval. We measured the activation of story-specific and schematic representations using fMRI while participants were presented with 16 stories and then recalled each of the narratives, and related these activations to memory for specific story details. We predicted that schema representations in medial prefrontal cortex (mPFC) would be correlated with successful recall of story details. In keeping with this prediction, an anterior mPFC region showed a significant correlation between activation of schema representations at encoding and subsequent behavioral recall performance; however, this mPFC region was not implicated in schema representation during retrieval. More generally, our analyses revealed largely distinct brain networks at encoding and retrieval in which schema activation was related to successful recall. These results provide new insight into when and where event knowledge can support narrative memory., Competing Interests: RM, KN, CB No competing interests declared, (© 2022, Masís-Obando et al.)

Recent fMRI studies of event segmentation have found that default mode regions represent high-level event structure during movie watching. In these regions, neural patterns are relatively stable during events and shift at event boundaries. Music, like narratives, contains hierarchical event structure (e.g., sections are composed of phrases). Here, we tested the hypothesis that brain activity patterns in default mode regions reflect the high-level event structure of music. We used fMRI to record brain activity from 25 participants (male and female) as they listened to a continuous playlist of 16 musical excerpts and additionally collected annotations for these excerpts by asking a separate group of participants to mark when meaningful changes occurred in each one. We then identified temporal boundaries between stable patterns of brain activity using a hidden Markov model and compared the location of the model boundaries to the location of the human annotations. We identified multiple brain regions with significant matches to the observer-identified boundaries, including auditory cortex, medial prefrontal cortex, parietal cortex, and angular gyrus. From these results, we conclude that both higher-order and sensory areas contain information relating to the high-level event structure of music. Moreover, the higher-order areas in this study overlap with areas found in previous studies of event perception in movies and audio narratives, including regions in the default mode network., (© 2022 Massachusetts Institute of Technology.)

The "Narratives" collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging., (© 2021. The Author(s).)

Learning about temporal structure is adaptive because it enables the generation of expectations. We examined how the brain uses experience in structured environments to anticipate upcoming events. During fMRI (functional magnetic resonance imaging), individuals watched a 90 s movie clip six times. Using a hidden Markov model applied to searchlights across the whole brain, we identified temporal shifts between activity patterns evoked by the first vs. repeated viewings of the movie clip. In many regions throughout the cortex, neural activity patterns for repeated viewings shifted to precede those of initial viewing by up to 15 s. This anticipation varied hierarchically in a posterior (less anticipation) to anterior (more anticipation) fashion. We also identified specific regions in which the timing of the brain's event boundaries was related to those of human-labeled event boundaries, with the timing of this relationship shifting on repeated viewings. With repeated viewing, the brain's event boundaries came to precede human-annotated boundaries by 1-4 s on average. Together, these results demonstrate a hierarchy of anticipatory signals in the human brain and link them to subjective experiences of events., Competing Interests: CL, MA, CB No competing interests declared, (© 2021, Lee et al.)

Through specific experiences, humans learn the relationships that underlie the structure of events in the world. Schema theory suggests that we organize this information in mental frameworks called "schemata," which represent our knowledge of the structure of the world. Generalizing knowledge of structural relationships to new situations requires role-filler binding, the ability to associate specific "fillers" with abstract "roles." For instance, when we hear the sentence Alice ordered a tea from Bob , the role-filler bindings customer:Alice , drink:tea and barista:Bob allow us to understand and make inferences about the sentence. We can perform these bindings for arbitrary fillers-we understand this sentence even if we have never heard the names Alice , tea , or Bob before. In this work, we define a model as capable of performing role-filler binding if it can recall arbitrary fillers corresponding to a specified role, even when these pairings violate correlations seen during training. Previous work found that models can learn this ability when explicitly told what the roles and fillers are, or when given fillers seen during training. We show that networks with external memory learn to bind roles to arbitrary fillers, without explicitly labeled role-filler pairs. We further show that they can perform these bindings on role-filler pairs that violate correlations seen during training, while retaining knowledge of training correlations. We apply analyses inspired by neural decoding to interpret what the networks have learned., Competing Interests: The authors declare that they have no competing interests., (© 2021 Chen et al.)

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be se amlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.

With advances in methods for collecting and analyzing fMRI data, there is a concurrent need to understand how to reliably evaluate and optimally use these methods. Simulations of fMRI data can aid in both the evaluation of complex designs and the analysis of data. We present fmrisim, a new Python package for standardized, realistic simulation of fMRI data. This package is part of BrainIAK: a recently released open-source Python toolbox for advanced neuroimaging analyses. We describe how to use fmrisim to extract noise properties from real fMRI data and then create a synthetic dataset with matched noise properties and a user-specified signal. We validate the noise generated by fmrisim to show that it can approximate the noise properties of real data. We further show how fmrisim can help researchers find the optimal design in terms of power. The fmrisim package holds promise for improving the design of fMRI experiments, which may facilitate both the pre-registration of such experiments as well as the analysis of fMRI data., Competing Interests: The authors declare there are no competing interests., (©2020 Ellis et al.)

Segmentation of continuous experience into discrete events is driven by rapid fluctuations in encoding stability at context shifts (i.e., event boundaries), yet the mechanisms underlying the online formation of event memories are poorly understood. We investigated the neural per-time point spatial similarity patterns of the scalp electrophysiological (EEG) activity of 30 human participants (male and female) watching a 50 min movie and found that event boundaries triggered the rapid reinstatement of the just-encoded movie event EEG patterns. We also found that the onset of memory reinstatement at boundary onset was accompanied by a left-lateralized anterior negative ERP effect, which likely reflects the detection of a shift in the narrative structure of the movie. A data-driven approach based on Hidden Markov modeling allowed us to detect event boundaries as shifts between stable patterns of brain EEG activity during encoding, and to identify their reactivation during a free recall task. These results provide the first neurophysiological underpinnings for how the memory systems segment a continuous long stream of experience into episodic events. SIGNIFICANCE OF STATEMENT Memory for specific episodic events are the building blocks of our autobiographical memory. However, it is still unclear how the memory systems structure the unfolding experience into discrete event units that can be understood and remembered at the long-term. Here, we show that the detection of context shifts, or event boundaries, during a 50 min movie viewing triggers the rapid memory reactivation of the just-encoded event to promote its successful encoding into long-term memory. By finding that memory reactivation, a neural mechanism critical for episodic memory formation and consolidation, takes place under these ecologically valid experimental circumstances, our results provide valuable insights into how the brain shapes the ongoing experience into episodic memories in the real-life., (Copyright © 2019 the authors.)

Understanding movies and stories requires maintaining a high-level situation model that abstracts away from perceptual details to describe the location, characters, actions, and causal relationships of the currently unfolding event. These models are built not only from information present in the current narrative, but also from prior knowledge about schematic event scripts, which describe typical event sequences encountered throughout a lifetime. We analyzed fMRI data from 44 human subjects (male and female) presented with 16 three-minute stories, consisting of four schematic events drawn from two different scripts (eating at a restaurant or going through the airport). Aside from this shared script structure, the stories varied widely in terms of their characters and storylines, and were presented in two highly dissimilar formats (audiovisual clips or spoken narration). One group was presented with the stories in an intact temporal sequence, while a separate control group was presented with the same events in scrambled order. Regions including the posterior medial cortex, medial prefrontal cortex (mPFC), and superior frontal gyrus exhibited schematic event patterns that generalized across stories, subjects, and modalities. Patterns in mPFC were also sensitive to overall script structure, with temporally scrambled events evoking weaker schematic representations. Using a Hidden Markov Model, patterns in these regions predicted the script (restaurant vs airport) of unlabeled data with high accuracy and were used to temporally align multiple stories with a shared script. These results extend work on the perception of controlled, artificial schemas in human and animal experiments to naturalistic perception of complex narratives. SIGNIFICANCE STATEMENT In almost all situations we encounter in our daily lives, we are able to draw on our schematic knowledge about what typically happens in the world to better perceive and mentally represent our ongoing experiences. In contrast to previous studies that investigated schematic cognition using simple, artificial associations, we measured brain activity from subjects watching movies and listening to stories depicting restaurant or airport experiences. Our results reveal a network of brain regions that is sensitive to the shared temporal structure of these naturalistic situations. These regions abstract away from the particular details of each story, activating a representation of the general type of situation being perceived., (Copyright © 2018 the authors 0270-6474/18/389689-11$15.00/0.)

Several research groups have shown how to map fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI data gathered from subjects watching an episode of BBCs Sherlock (Chen et al., 2017), and learn bidirectional mappings between fMRI responses and natural language representations. By leveraging data from multiple subjects watching the same movie, we were able to perform scene classification with 72% accuracy (random guessing would give 4%) and scene ranking with average rank in the top 4% (random guessing would give 50%). The key ingredients underlying this high level of performance are (a) the use of the Shared Response Model (SRM) and its variant SRM-ICA (Chen et al., 2015; Zhang et al., 2016) to aggregate fMRI data from multiple subjects, both of which are shown to be superior to standard PCA in producing low-dimensional representations for the tasks in this paper; (b) a sentence embedding technique adapted from the natural language processing (NLP) literature (Arora et al., 2017) that produces semantic vector representation of the annotations; (c) using previous timestep information in the featurization of the predictor data. These optimizations in how we featurize the fMRI data and text annotations provide a substantial improvement in classification performance, relative to standard approaches., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information., Competing Interests: IG, MG, CB, LF, DB, CB No competing interests declared

During realistic, continuous perception, humans automatically segment experiences into discrete events. Using a novel model of cortical event dynamics, we investigate how cortical structures generate event representations during narrative perception and how these events are stored to and retrieved from memory. Our data-driven approach allows us to detect event boundaries as shifts between stable patterns of brain activity without relying on stimulus annotations and reveals a nested hierarchy from short events in sensory regions to long events in high-order areas (including angular gyrus and posterior medial cortex), which represent abstract, multimodal situation models. High-order event boundaries are coupled to increases in hippocampal activity, which predict pattern reinstatement during later free recall. These areas also show evidence of anticipatory reinstatement as subjects listen to a familiar narrative. Based on these results, we propose that brain activity is naturally structured into nested events, which form the basis of long-term memory representations., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Understanding human-object interactions is critical for extracting meaning from everyday visual scenes and requires integrating complex relationships between human pose and object identity into a new percept. To understand how the brain builds these representations, we conducted 2 fMRI experiments in which subjects viewed humans interacting with objects, noninteracting human-object pairs, and isolated humans and objects. A number of visual regions process features of human-object interactions, including object identity information in the lateral occipital complex (LOC) and parahippocampal place area (PPA), and human pose information in the extrastriate body area (EBA) and posterior superior temporal sulcus (pSTS). Representations of human-object interactions in some regions, such as the posterior PPA (retinotopic maps PHC1 and PHC2) are well predicted by a simple linear combination of the response to object and pose information. Other regions, however, especially pSTS, exhibit representations for human-object interaction categories that are not predicted by their individual components, indicating that they encode human-object interactions as more than the sum of their parts. These results reveal the distributed networks underlying the emergent representation of human-object interactions necessary for social perception., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

A number of regions in the human brain are known to be involved in processing natural scenes, but the field has lacked a unifying framework for understanding how these different regions are organized and interact. We provide evidence from functional connectivity and meta-analyses for a new organizational principle, in which scene processing relies upon two distinct networks that split the classically defined parahippocampal place area (PPA). The first network of strongly connected regions consists of the occipital place area/transverse occipital sulcus and posterior PPA, which contain retinotopic maps and are not strongly coupled to the hippocampus at rest. The second network consists of the caudal inferior parietal lobule, retrosplenial complex, and anterior PPA, which connect to the hippocampus (especially anterior hippocampus), and are implicated in both visual and nonvisual tasks, including episodic memory and navigation. We propose that these two distinct networks capture the primary functional division among scene-processing regions, between those that process visual features from the current view of a scene and those that connect information from a current scene view with a much broader temporal and spatial context. This new framework for understanding the neural substrates of scene-processing bridges results from many lines of research, and makes specific functional predictions.

Neuroimaging studies of mood disorders demonstrate abnormalities in brain regions implicated in reward processing. However, there is a paucity of research investigating how social rewards affect reward circuit activity in these disorders. Here, we evaluated the relationship of both diagnostic category and dimensional depression severity to reward system function in bipolar and unipolar depression. In total, 86 adults were included, including 24 patients with bipolar depression, 24 patients with unipolar depression, and 38 healthy comparison subjects. Participants completed a social reward task during 3T BOLD fMRI. On average, diagnostic groups did not differ in activation to social reward. However, greater depression severity significantly correlated with reduced bilateral ventral striatum activation to social reward in the bipolar depressed group, but not the unipolar depressed group. In addition, decreased left orbitofrontal cortical activation correlated with more severe symptoms in bipolar depression, but not unipolar depression. These differential dimensional effects resulted in a significant voxelwise group by depression severity interaction. Taken together, these results provide initial evidence that deficits in social reward processing are differentially related to depression severity in the two disorders., Competing Interests: The authors declare no conflict of interest. Dr. Thase reports no conflicts directly pertaining to this research. Over the past 36 months, he reports the following relationships: Alkermes, AstraZeneca, Bristol-Myers Squibb, Cerecor, Eli Lilly, Dey Pharma, Forest Laboratories, Gerson Lehman Group, Guidepoint Global, H. Lundbeck A/S, MedAvante, Merck, Neuronetics, Otsuka, Ortho-McNeil, Pamlab, Pfizer, PGx, Shire, Sunovion, Super-nus, Takeda, and Transcept Pharmaceuticals., (Published by Elsevier Ireland Ltd.)

Peripherally presented stimuli evoke stronger activity in scene-processing regions than foveally presented stimuli, suggesting that scene understanding is driven largely by peripheral information. We used functional MRI to investigate whether functional connectivity evoked during natural perception of audiovisual movies reflects this peripheral bias. For each scene-sensitive region--the parahippocampal place area (PPA), retrosplenial cortex, and occipital place area--we computed two measures: the extent to which its activity could be predicted by V1 activity (connectivity strength) and the eccentricities within V1 to which it was most closely related (connectivity profile). Scene regions were most related to peripheral voxels in V1, but the detailed nature of this connectivity varied within and between these regions. The retrosplenial cortex showed the most consistent peripheral bias but was less predictable from V1 activity, while the occipital place area was related to a wider range of eccentricities and was strongly coupled to V1. We divided the PPA along its posterior-anterior axis into retinotopic maps PHC1, PHC2, and anterior PPA, and found that a peripheral bias was detectable throughout all subregions, though the anterior PPA showed a less consistent relationship to eccentricity and a substantially weaker overall relationship to V1. We also observed an opposite foveal bias in object-perception regions including the lateral occipital complex and fusiform face area. These results show a fine-scale relationship between eccentricity biases and functional correlation during natural perception, giving new insight into the structure of the scene-perception network.

How do we know that a kitchen is a kitchen by looking? Traditional models posit that scene categorization is achieved through recognizing necessary and sufficient features and objects, yet there is little consensus about what these may be. However, scene categories should reflect how we use visual information. Therefore, we test the hypothesis that scene categories reflect functions, or the possibilities for actions within a scene. Our approach is to compare human categorization patterns with predictions made by both functions and alternative models. We collected a large-scale scene category distance matrix (5 million trials) by asking observers to simply decide whether 2 images were from the same or different categories. Using the actions from the American Time Use Survey, we mapped actions onto each scene (1.4 million trials). We found a strong relationship between ranked category distance and functional distance (r = .50, or 66% of the maximum possible correlation). The function model outperformed alternative models of object-based distance (r = .33), visual features from a convolutional neural network (r = .39), lexical distance (r = .27), and models of visual features. Using hierarchical linear regression, we found that functions captured 85.5% of overall explained variance, with nearly half of the explained variance captured only by functions, implying that the predictive power of alternative models was because of their shared variance with the function-based model. These results challenge the dominant school of thought that visual features and objects are sufficient for scene categorization, suggesting instead that a scene's category may be determined by the scene's function., ((c) 2015 APA, all rights reserved).)

A common goal in biological sciences is to model a complex web of connections using a small number of interacting units. We present a general approach for dividing up elements in a spatial map based on their connectivity properties, allowing for the discovery of local regions underlying large-scale connectivity matrices. Our method is specifically designed to respect spatial layout and identify locally-connected clusters, corresponding to plausible coherent units such as strings of adjacent DNA base pairs, subregions of the brain, animal communities, or geographic ecosystems. Instead of using approximate greedy clustering, our nonparametric Bayesian model infers a precise parcellation using collapsed Gibbs sampling. We utilize an infinite clustering prior that intrinsically incorporates spatial constraints, allowing the model to search directly in the space of spatially-coherent parcellations. After showing results on synthetic datasets, we apply our method to both functional and structural connectivity data from the human brain. We find that our parcellation is substantially more effective than previous approaches at summarizing the brain's connectivity structure using a small number of clusters, produces better generalization to individual subject data, and reveals functional parcels related to known retinotopic maps in visual cortex. Additionally, we demonstrate the generality of our method by applying the same model to human migration data within the United States. This analysis reveals that migration behavior is generally influenced by state borders, but also identifies regional communities which cut across state lines. Our parcellation approach has a wide range of potential applications in understanding the spatial structure of complex biological networks.

The Parahippocampal Place Area (PPA) has traditionally been considered a homogeneous region of interest, but recent evidence from both human studies and animal models has suggested that PPA may be composed of functionally distinct subunits. To investigate this hypothesis, we utilize a functional connectivity measure for fMRI that can estimate connectivity differences at the voxel level. Applying this method to whole-brain data from two experiments, we provide the first direct evidence that anterior and posterior PPA exhibit distinct connectivity patterns, with anterior PPA more strongly connected to regions in the default mode network (including the parieto-medial temporal pathway) and posterior PPA more strongly connected to occipital visual regions. We show that object sensitivity in PPA also has an anterior-posterior gradient, with stronger responses to abstract objects in posterior PPA. These findings cast doubt on the traditional view of PPA as a single coherent region, and suggest that PPA is composed of one subregion specialized for the processing of low-level visual features and object shape, and a separate subregion more involved in memory and scene context., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Discovering functional connectivity between and within brain regions is a key concern in neuroscience. Due to the noise inherent in fMRI data, it is challenging to characterize the properties of individual voxels, and current methods are unable to flexibly analyze voxel-level connectivity differences. We propose a new functional connectivity method which incorporates a spatial smoothness constraint using regularized optimization, enabling the discovery of voxel-level interactions between brain regions from the small datasets characteristic of fMRI experiments. We validate our method in two separate experiments, demonstrating that we can learn coherent connectivity maps that are consistent with known results. First, we examine the functional connectivity between early visual areas V1 and VP, confirming that this connectivity structure preserves retinotopic mapping. Then, we show that two category-selective regions in ventral cortex - the Parahippocampal Place Area (PPA) and the Fusiform Face Area (FFA) - exhibit an expected peripheral versus foveal bias in their connectivity with visual area hV4. These results show that our approach is powerful, widely applicable, and capable of uncovering complex connectivity patterns with only a small amount of input data., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Ziprasidone (Geodon) is a relatively new atypical antipsychotic medication with a unique pharmacological profile. It is indicated for the treatment of schizophrenia, but has also often been used off-label for other uses. This review summarizes its important properties, specifically the pharmacodynamic parameters, receptor-binding profile and relevance to clinical outcomes, side effects, and potential for drug-drug interactions and established clinical indications. Novel therapeutic applications and relevant clinical trials or reports are also examined. The authors review the current market and speculate on likely changes in 5 years.

Objective: The relationship between attention deficit hyperactivity disorder (ADHD) and earlier age at onset of affective illness was examined in probands with a history of bipolar disorder., Method: The authors assessed 56 adult bipolar subjects. Those with a history of childhood ADHD (N=8) were age and sex matched with bipolar subjects without a history of childhood ADHD (N=8)., Results: The age at onset of the first affective episode was lower for the subjects with bipolar disorder and a history of childhood ADHD (mean=12.1 years, SD=4.6) than for those without a history of childhood ADHD (mean=20. 0 years, SD=11.3)., Conclusions: ADHD in children of bipolar probands might identify children at highest risk for development of bipolar disorder.

Lack of insight complicates the evaluation and treatment of patients with psychotic and affective disorders. No studies of insight in seasonal affective disorder (SAD) have been reported. Thirty patients with SAD diagnosed by the Structured Clinical Interview for DSM-III-R but no other axis I conditions were treated short-term with light-therapy. Insight was measured with the Scale to Assess Unawareness of Mental Disorder (SUMD) as modified by the authors to assess the self-report of insight into depressive symptoms. Increasing scores (1 to 5) indicated increasing unawareness of illness (i.e., less insight). SAD patients displayed a moderate amount of insight when depressed (mean SUMD score, 2.5). When recovered, they showed no significant change in insight into past depressive symptoms (mean SUMD score, 2.8). Greater insight into current depressive symptoms correlated with more depressive symptoms on the Hamilton Rating Scale for Depression score ([HRSD] r = .35, P < .05). In conclusion, SAD patients possess a moderate amount of insight into depressive symptoms that does not change after recovery, a result in agreement with studies of insight in psychosis and mania. Further, in SAD, increased severity of illness may be associated with increased insight into depressive symptoms, consistent with the hypothesis of depressive realism.

Although akathisia is most commonly associated with neuroleptic medication, few cases of paroxetine-induced akathisia have been reported. A review of the authors' charts (C.F.B., A.N., S.N.G., and G.S.S.) was conducted to determine an estimated incidence for paroxetine-induced akathisia. Three cases of akathisia were reported in 67 patients treated with paroxetine. A case of akathisia secondary to paroxetine in an 18-year-old female is presented. Given the potential untoward effects of this syndrome, early diagnosis is essential. Clinical presentations and differential diagnoses are discussed.

This article describes a cognitive-behavioral program for substance abusers which was first implemented in the Baltimore City Jail in 1987. Similar but separate programs are provided for male and female inmates, consisting of twelve to sixteen contact hours over three to four weeks. In addition to conventional drug and alcohol information (physiologic and psychological effects, treatment options), the program emphasizes cognitive and behavioral skills which can prevent substance abuse, including training in consequential thinking, and stress and anger management. Over a two-year period, 607 males and 131 females were served, of whom 429 (59%) completed the entire program. Both males and females showed statistically significant improvement from pretest to posttest in all knowledge areas. Inmates gave high ratings to the program and group leaders reported substantial change in client attitudes toward drug and alcohol use. Knowledge scores at the end of the program were highest for those who scored higher at pretest, rated their group leader higher, and were rated by their group leader as more active participants. Client participation was the strongest predictor of program outcome.

Clonazepam 0.5 mg was evaluated in a sleep laboratory study of 6 insomniac patients. The 16-night protocol consisted of 4 placebo-baseline nights, 7 nights of drug administration and 5 placebo-withdrawal nights. Clonazepam produced a significant decrease in total wake time initially (nights 5-7), as well as with continued administration (nights 9-11). With later but not immediate withdrawal, significant rebound insomnia occurred, on the 3rd withdrawal night, both wake time after sleep onset and total wake time increased markedly, with the latter significantly increased. These findings are discussed in light of clonazepam's increasing use for panic disorder; specifically, due to its maintained efficacy, it has the advantage of avoiding interdose rebound anxiety which is frequently reported with use of alprazolam.