Using fMRI Brain Activation to Identify Cognitive States Associated with Perception of Tools and Dwellings

Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated with a 4s viewing of an individual line drawing (1 of 10 familiar objects, 5 tools and 5 dwellings, such as a hammer or a castle). Here we demonstrate the ability to reliably (1) identify which of the 10 drawings a participant was viewing, based on that participant's characteristic whole-brain neural activation patterns, excluding visual areas; (2) identify the category of the object with even higher accuracy, based on that participant's activation; and (3) identify, for the first time, both individual objects and the category of the object the participant was viewing, based only on other participants' activation patterns. The voxels important for category identification were located similarly across participants, and distributed throughout the cortex, focused in ventral temporal perceptual areas but also including more frontal association areas (and somewhat left-lateralized). These findings indicate the presence of stable, distributed, communal, and identifiable neural states corresponding to object concepts

Finding the engram.

Many attempts have been made to localize the physical trace of a memory, or engram, in the brain. However, until recently, engrams have remained largely elusive. In this Review, we develop four defining criteria that enable us to critically assess the recent progress that has been made towards finding the engram. Recent \u27capture\u27 studies use novel approaches to tag populations of neurons that are active during memory encoding, thereby allowing these engram-associated neurons to be manipulated at later times. We propose that findings from these capture studies represent considerable progress in allowing us to observe, erase and express the engram

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Retrieval induces adaptive forgetting of competing memories via cortical pattern suppression

Remembering a past experience can, surprisingly, cause forgetting. Forgetting arises when other competing traces interfere with retrieval and inhibitory control mechanisms are engaged to suppress the distraction they cause. This form of forgetting is considered to be adaptive because it reduces future interference. The effect of this proposed inhibition process on competing memories has, however, never been observed, as behavioral methods are 'blind' to retrieval dynamics and neuroimaging methods have not isolated retrieval of individual memories. We developed a canonical template tracking method to quantify the activation state of individual target memories and competitors during retrieval. This method revealed that repeatedly retrieving target memories suppressed cortical patterns unique to competitors. Pattern suppression was related to engagement of prefrontal regions that have been implicated in resolving retrieval competition and, critically, predicted later forgetting. Thus, our findings demonstrate a cortical pattern suppression mechanism through which remembering adaptively shapes which aspects of our past remain accessible

Context in free recall: multi-voxel pattern analysis of fMRI

Several researchers (e.g., Howard Kahana, 2002) have proposed that recalling an event is bound up with recall of that event's surrounding context, and that retrieved context information can be used to cue memory for other items from that context. In this study, we sought evidence for this contextual reinstatement process using fMRI. Specifically, we wanted to know whether the task being performed when forming a memory would be recalled along with that memory, and how this would influence subsequent recalls. Subjects studied lists of 24 words, performing either a size, animacy or pleasantness judgment task on each word. After a series of arithmetic distractors, subjects were asked to recall out loud and in any order the words from the most recent list. Since subjects were being scanned during both study and recall phases, we trained a classifier on the study period to distinguish which of the three tasks were being performed. We then tested this classifier during recall to estimate the degree to which each task representation was active in the subject's mind, moment by moment (Polyn et al, 2005). To analyze the recall data, we labelled each recall with its judgment task from the study period. These were predicted better than chance by the classifier's estimates of task activity at recall. We broke the data down further, looking at the transitions from one recall to the next. We found that high classifier activity for one kind of task judgment indicated that the next recall would be another item from that task, and that the inter-response latency would be small. In other words, a highly active task representation would facilitate recalls of other items from the same task. These results support the contextual reinstatement theory, suggesting that reinstating the context surrounding an event improves recall of other items that were studied in that context