A model for visual memory encoding

Openness to Experience is associated with neural and performance measures of memory in older adults

Age-related decline in episodic memory performance is a well-replicated finding across numerous studies. Recent studies focusing on aging and individual differences found that the Big Five personality trait Openness to Experience (hereafter: Openness) is associated with better episodic memory performance in older adults, but the associated neural mechanisms are largely unclear. Here, we investigated the relationship between Openness and memory network function in a sample of 352 participants (143 older adults, 50-80 years; 209 young adults, 18-35 years). Participants underwent functional magnetic resonance imaging (fMRI) during a visual memory encoding task. Functional memory brain-network integrity was assessed using the similarity of activations during memory encoding (SAME) scores, which reflect the similarity of a participant's memory network activity compared to prototypical fMRI activity patterns of young adults. Openness was assessed using the NEO Five-Factor Inventory. Older vs young adults showed lower memory performance and higher deviation of fMRI activity patterns (i.e. lower SAME scores). Specifically in older adults, high Openness was associated with better memory performance, and mediation analysis showed that this relationship was partially mediated by higher SAME scores. Our results suggest that trait Openness may constitute a protective factor in cognitive aging by better preservation of the brain's memory network.

Decoding declarative memory process for predicting memory retrieval based on source localization

Many studies have focused on understanding memory processes due to their importance in daily life. Differences in timing and power spectra of brain signals during encoding task have been linked to later remembered items and were recently used to predict memory retrieval performance. However, accuracies remain low when using non-invasive methods for acquiring brain signals, mainly due to the low spatial resolution. This study investigates the prediction of successful retrieval using estimated source activity corresponding either to cortical or subcortical structures through source localization. Electroencephalogram (EEG) signals were recorded while participants performed a declarative memory task. Frequency-time analysis was performed using signals from encoding and retrieval tasks to confirm the importance of neural oscillations and their relationship with later remembered and forgotten items. Significant differences in the power spectra between later remembered and forgotten items were found before and during the presentation of the stimulus in the encoding task. Source activity estimation revealed differences in the beta band power over the medial parietal and medial prefrontal areas prior to the presentation of the stimulus, and over the cuneus and lingual areas during the presentation of the stimulus. Additionally, there were significant differences during the stimuli presentation during the retrieval task. Prediction of later remembered items was performed using surface potentials and estimated source activity. The results showed that source localization increases classification performance compared to the one using surface potentials. These findings support the importance of incorporating spatial features of neural activity to improve the prediction of memory retrieval.

Comparing the reliability of different ICA algorithms for fMRI analysis

Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of time courses. Some popular ICA algorithms such as Infomax or FastICA generate different results after repeated analysis from the same data volume, which is generally acknowledged as a drawback for ICA approaches. The reliability of some ICA algorithms has been explored by methods such as ICASSO and RAICAR (ranking and averaging independent component analysis by reproducibility). However, the exact algorithmic reliability of different ICA algorithms has not been examined and compared with each other. Here, the quality index generated with ICASSO and spatial correlation coefficients were used to examine the reliability of different ICA algorithms. The results demonstrated that Infomax running 10 times with ICASSO could generate consistent independent components from fMRI data sets.

Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network

The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.

Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data

Resting-state functional connectivity hypernetworks, in which multiple nodes can be connected, are an effective technique for diagnosing brain disease and performing classification research. Conventional functional hypernetworks can characterize the complex interactions within the human brain in a static form. However, an increasing body of evidence demonstrates that even in a resting state, neural activity in the brain still exhibits transient and subtle dynamics. These dynamic changes are essential for understanding the basic characteristics underlying brain organization and may correlate significantly with the pathological mechanisms of brain diseases. Therefore, considering the dynamic changes of functional connections in the resting state, we proposed methodology to construct resting state high-order functional hyper-networks (rs-HOFHNs) for patients with depression and normal subjects. Meanwhile, we also introduce a novel property (the shortest path) to extract local features with traditional local properties (cluster coefficients). A subgraph feature-based method was introduced to characterize information relating to global topology. Two features, local features and subgraph features that showed significant differences after feature selection were subjected to multi-kernel learning for feature fusion and classification. Compared with conventional hyper network models, the high-order hyper network obtained the best classification performance, 92.18%, which indicated that better classification performance can be achieved if we needed to consider multivariate interactions and the time-varying characteristics of neural interaction simultaneously when constructing a network.

AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction

Provenances are a research focus of neuroimaging resources sharing. An amount of work has been done to construct high-quality neuroimaging provenances in a standardized and convenient way. However, besides existing processed-based provenance extraction methods, open research sharing in computational neuroscience still needs one way to extract provenance information from rapidly growing published resources. This paper proposes a literature mining-based approach for research sharing-oriented neuroimaging provenance construction. A group of neuroimaging event-containing attributes are defined to model the whole process of neuroimaging researches, and a joint extraction model based on deep adversarial learning, called AT-NeuroEAE, is proposed to realize the event extraction in a few-shot learning scenario. Finally, a group of experiments were performed on the real data set from the journal PLOS ONE. Experimental results show that the proposed method provides a practical approach to quickly collect research information for neuroimaging provenance construction oriented to open research sharing.

Recent Advances in Neuroimaging of Epilepsy

Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.

A Functional MRI Paradigm for Efficient Mapping of Memory Encoding Across Sensory Conditions

We introduce a new and time-efficient memory-encoding paradigm for functional magnetic resonance imaging (fMRI). This paradigm is optimized for mapping multiple contrasts using a mixed design, using auditory (environmental/vocal) and visual (scene/face) stimuli. We demonstrate that the paradigm evokes robust neuronal activity in typical sensory and memory networks. We were able to detect auditory and visual sensory-specific encoding activities in auditory and visual cortices. Also, we detected stimulus-selective activation in environmental-, voice-, scene-, and face-selective brain regions (parahippocampal place and fusiform face area). A subsequent recognition task allowed the detection of sensory-specific encoding success activity (ESA) in both auditory and visual cortices, as well as sensory-unspecific positive ESA in the hippocampus. Further, sensory-unspecific negative ESA was observed in the precuneus. Among others, the parallel mixed design enabled sustained and transient activity comparison in contrast to rest blocks. Sustained and transient activations showed great overlap in most sensory brain regions, whereas several regions, typically associated with the default-mode network, showed transient rather than sustained deactivation. We also show that the use of a parallel mixed model had relatively little influence on positive or negative ESA. Together, these results demonstrate a feasible, versatile, and brief memory-encoding task, which includes multiple sensory stimuli to guarantee a comprehensive measurement. This task is especially suitable for large-scale clinical or population studies, which aim to test task-evoked sensory-specific and sensory-unspecific memory-encoding performance as well as broad sensory activity across the life span within a very limited time frame.

Pattern separation beyond the hippocampus: A high-resolution whole-brain investigation of mnemonic discrimination in healthy adults

Episodic memory depends on the computational process of pattern separation in order to establish distinct memory representations of similar episodes. Studies of pattern separation in humans rely on mnemonic discrimination tasks, which have been shown to tax hippocampal-dependent pattern separation. Although previous neuroimaging research has focused on hippocampal processing, little is known about how other brain regions, known to be involved in recognition memory performance, are involved in mnemonic discrimination tasks. Conversely, neuroimaging studies of pattern separation with whole-brain coverage lack spatial resolution to localize activation to hippocampal subfields. In this study, 48 healthy young adult participants underwent whole-brain high-resolution functional MRI (fMRI) scanning while completing a mnemonic discrimination task. A priori region-of-interest analyses revealed activation patterns consistent with pattern separation in distinct hippocampal subregions, particularly in the subiculum. Connectivity analyses revealed a network of cortical regions consistent with the memory retrieval network where fMRI activation was correlated with hippocampal activation. An exploratory whole-brain analysis revealed widespread activation differentially associated with performance of the mnemonic discrimination task. Taken together, these results suggest that a network of brain regions contribute to mnemonic discrimination performance, with the hippocampus and parahippocampal cortex as a hub in the network displaying clear signals consistent with pattern separation and regions such as the dorsal medial prefrontal cortex particularly important for successful lure discrimination.

Benefits of glioma resection in the corpus callosum

Due to anticipated postoperative neuropsychological sequelae, patients with gliomas infiltrating the corpus callosum rarely undergo tumor resection and mostly present in a poor neurological state. We aimed at investigating the benefit of glioma resection in the corpus callosum, hypothesizing neuropsychological deficits were mainly caused by tumor presence. Between 01/2017 and 1/2020, 21 patients who underwent glioma resection in the corpus callosum were prospectively enrolled into this study. Neuropsychological function was assessed preoperatively, before discharge and after 6 months. Gross total tumor resection was possible in 15 patients, and in 6 patients subtotal tumor resection with a tumor reduction of 97.7% could be achieved. During a median observation time of 12.6 months 9 patients died from glioblastoma after a median of 17 months. Preoperatively, all cognitive domains were affected in up to two thirds of patients, who presented a median KPS of 100% (range 60–100%). After surgery, the proportion of impaired patients increased in all neurocognitive domains. Most interestingly, after 6 months, significantly fewer patients showed impairments in attention, executive functioning, memory and depression, which are domains considered crucial for everyday functionality. Thus, the results of our study strongly support our hypothesis that in patients with gliomas infiltrating the corpus callosum the benefit of tumor resection might outweigh morbidity.

Large-Scale Brain Networks Underlying Successful and Unsuccessful Encoding, Maintenance, and Retrieval of Everyday Scenes in Visuospatial Working Memory

Recent research on working memory (WM) identified the contribution of several large-scale brain networks operating during WM tasks, such as the frontoparietal attention network (AN), the default mode network (DMN), and the salience network (SN). To date, however, the dynamical interplay among these networks is largely unexplored during successful or unsuccessful WM performance, especially with complex and ecological stimuli. Here we systematically characterized the selective contribution of these networks during a visuospatial WM task requiring the encoding, maintenance and retrieval of real-life scenes. While undergoing fMRI scans, participants were presented with everyday life visual scenes for 4 s (encoding phase). After a delay of 8 s (maintenance phase), participants were presented with a target-object extracted from the previous scene. Participants had to judge whether the target-object was presented at the same or in a different location compared to the original scene (retrieval phase) and then provide a confidence judgment. Using the independent component analysis (ICA), we found that subsequent remembering was associated with the activity of the AN at encoding, the attention and SN at maintenance, plus the visual network at retrieval. Conversely, subsequent forgetting was associated with the activity of the DMN at maintenance, and the SN at retrieval. Overall, these findings reveal a dynamical interplay between large-scale brain networks during visuospatial WM performance related to complex, real-life stimuli.

Neural signatures of co-occurring reading and mathematical difficulties

Impaired abilities in multiple domains is common in children with learning difficulties. Co-occurrence of low reading and mathematical abilities (LRLM) appears in almost every second child with learning difficulties. However, little is known regarding the neural bases of this combination. Leveraging a unique and tightly controlled sample including children with LRLM, isolated low reading ability (LR), and isolated low mathematical ability (LM), we uncover a distinct neural signature in children with co-occurring low reading and mathematical abilities differentiable from LR and LM. Specifically, we show that LRLM is neuroanatomically distinct from both LR and LM based on reduced cortical folding of the right parahippocampal gyrus, a medial temporal lobe region implicated in visual associative learning. LRLM children were further distinguished from LR and LM by patterns of intrinsic functional connectivity between parahippocampal gyrus and brain circuitry underlying reading and numerical quantity processing. Our results critically inform cognitive and neural models of LRLM by implicating aberrations in both domain-specific and domain-general brain regions involved in reading and mathematics. More generally, our results provide the first evidence for distinct multimodal neural signatures associated with LRLM, and suggest that this population displays an independent phenotype of learning difficulty that cannot be explained simply as a combination of isolated low reading and mathematical abilities.

A contemporary framework of language processing in the human brain in the context of preoperative and intraoperative language mapping

INTRODUCTION

The emergence of advanced in vivo neuroimaging methods has redefined the understanding of brain function with a shift from traditional localizationist models to more complex and widely distributed neural networks. In human language processing, the traditional localizationist models of Wernicke and Broca have fallen out of favor for a dual-stream processing system involving complex networks organized over vast areas of the dominant hemisphere. The current review explores the cortical function and white matter connections of human language processing, as well as their relevance to surgical planning.

METHODS

We performed a systematic review of the literature with narrative data analysis.

RESULTS

Although there is significant heterogeneity in the literature over the past century of exploration, modern evidence provides new insight into the true cortical function and white matter anatomy of human language. Intraoperative data and postoperative outcome studies confirm a widely distributed language network extending far beyond the traditional cortical areas of Wernicke and Broca.

CONCLUSIONS

The anatomic distribution of language networks, based on current theories, is explored to present a modern and clinically relevant interpretation of language function. Within this framework, we present current knowledge regarding the known effects of damage to both cortical and subcortical components of these language networks. Ideally, we hope this framework will provide a common language for which to base future clinical studies in human language function.

The effect of medial temporal lobe epilepsy on visual memory encoding

Effective visual memory encoding, a function important for everyday functioning, relies on episodic and semantic memory processes. In patients with medial temporal lobe epilepsy (MTLE), memory deficits are common as the structures typically involved in seizure generation are also involved in acquisition, maintenance, and retrieval of episodic memories. In this study, we used group independent component analysis (GICA) combined with Granger causality analysis to investigate the neuronal networks involved in visual memory encoding during a complex fMRI scene-encoding task in patients with left MTLE (LMTLE; N=28) and in patients with right MTLE (RMTLE; N=18). Additionally, we built models of memory encoding in LMTLE and RMTLE and compared them with a model of healthy memory encoding (Nenert et al., 2014). For those with LMTLE, we identified and retained for further analyses and model generation 7 ICA task-related components that were attributed to four different networks: the frontal and posterior components of the DMN, visual network, auditory-insular network, and an "other" network. For those with RMTLE, ICA produced 9 task-related components that were attributed to the somatosensory and cerebellar networks in addition to the same networks as in patients with LMTLE. Granger causality analysis revealed group differences in causality relations within the visual memory network and MTLE-related deviations from normal network function. Our results demonstrate differences in the networks for visual memory encoding between those with LMTLE and those with RMTLE. Consistent with previous studies, the organization of memory encoding is dependent on laterality of seizure focus and may be mediated by functional reorganization in chronic epilepsy. These differences may underlie the observed differences in memory abilities between patients with LMTLE and patients with RMTLE and highlight the modulating effects of epilepsy on the network for memory encoding.

Age related-changes in the neural basis of self-generation in verbal paired associate learning

Verbal information is better retained when it is self-generated rather than when it is received passively. The application of self-generation procedures has been found to improve memory in healthy elderly and in individuals with impaired cognition. Overall, the available studies support the notion that active participation in verbal encoding engages memory mechanisms that supplement those used during passive observation. Thus, the objective of this study was to investigate the age-related changes in the neural mechanisms involved in the encoding of paired-associates using a self-generation method that has been shown to improve memory performance across the lifespan. Subjects were 113 healthy right-handed adults (Edinburgh Handedness Inventory >50; 67 females) ages 18–76, native speakers of English with no history of neurological or psychiatric disorders. Subjects underwent fMRI at 3 T while performing didactic learning (“read”) or self-generation learning (“generate”) of 30 word pairs per condition. After fMRI, recognition memory for the second word in each pair was evaluated outside of the scanner. On the post-fMRI testing more “generate” words were correctly recognized than “read” words (p < 0.001) with older adults recognizing the “generated” words less accurately (p < 0.05). Independent component analysis of fMRI data identified task-related brain networks. Several components were positively correlated with the task reflecting multiple cognitive processes involved in self-generated encoding; other components correlated negatively with the task, including components of the default-mode network. Overall, memory performance on generated words decreased with age, but the benefit from self-generation remained consistently significant across ages. Independent component analysis of the neuroimaging data revealed an extensive set of components engaged in self-generation learning compared with didactic learning, and identified areas that were associated with age-related changes independent of performance.

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Verbal information is better retained when self-generated vs. received passively.

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Application of self-generation is associated with better retention across ages.

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Generated words were retained better than read words.

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Several components of network for word generation were identified.

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Age-associated changes within the network are discussed.