scholarly journals Distributed Patterns of Functional Connectivity Underlie Individual Differences in Long-Term Memory Forgetting

2021 ◽  
Author(s):  
Yinan Xu ◽  
Chantel S. Prat ◽  
Florian Sense ◽  
Hedderik van Rijn ◽  
Andrea Stocco
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Formal Model ◽  
Machine Learning Techniques ◽  
Connectivity Pattern ◽  
Long Term Memory ◽  
Resting State Functional Connectivity ◽  
Term Memory

Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade and, without such a measure, it is hard to identify the corresponding neural correlates. This study addresses this problem using a combination of individual differences, model-based inferences, and resting-state functional connectivity. The individual-specific values of rate of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to data from an adaptive fact learning task. Individual rates of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine-learning techniques to identify the most predictive and generalizable features. Consistent with the existing literature, our results identified a sparse, distributed network of cortical and subcortical regions that underlies forgetting in LTM. Cross-validation showed that individual rates of forgetting were predicted with high accuracy (r = .96) from this connectivity pattern alone. These results open up new opportunities for the study of individual differences in LTM function and dysfunction.

scholarly journals Temporal–prefrontal cortical network for discrimination of valuable objects in long-term memory

2018 ◽  
Vol 115 (9) ◽  
pp. E2135-E2144 ◽  
Author(s):  
Ali Ghazizadeh ◽  
Whitney Griggs ◽  
David A. Leopold ◽  
Okihide Hikosaka
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
High Capacity ◽  
Long Term Memory ◽  
Gaze Behavior ◽  
Resting State Functional Connectivity ◽  
Term Memory ◽  
Term Retention ◽  
Long Term Retention

Remembering and discriminating objects based on their previously learned values are essential for goal-directed behaviors. While the cerebral cortex is known to contribute to object recognition, surprisingly little is known about its role in retaining long-term object–value associations. To address this question, we trained macaques to arbitrarily associate small or large rewards with many random fractal objects (>100) and then used fMRI to study the long-term retention of value-based response selectivity across the brain. We found a pronounced long-term value memory in core subregions of temporal and prefrontal cortex where, several months after training, fractals previously associated with high reward (“good” stimuli) elicited elevated fMRI responses compared with those associated with low reward (“bad” stimuli). Similar long-term value-based modulation was also observed in subregions of the striatum, amygdala, and claustrum, but not in the hippocampus. The value-modulated temporal–prefrontal subregions showed strong resting-state functional connectivity to each other. Moreover, for areas outside this core, the magnitude of long-term value responses was predicted by the strength of resting-state functional connectivity to the core subregions. In separate testing, free-viewing gaze behavior indicated that the monkeys retained stable long-term memory of object value. These results suggest an implicit and high-capacity memory mechanism in the temporal–prefrontal circuitry and its associated subcortical regions for long-term retention of object-value memories that can guide value-oriented behavior.

scholarly journals Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory

2021 ◽  
pp. 1-18
Author(s):  
Qi Lin ◽  
Kwangsun Yoo ◽  
Xilin Shen ◽  
Todd R. Constable ◽  
Marvin M. Chun
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Recognition Memory ◽  
Memory Model ◽  
Long Term Memory ◽  
Whole Brain ◽  
Term Memory ◽  
Back Task

Abstract What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n-back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n-back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa. Functional connectivity only within regions traditionally associated with LTM formation, such as the medial temporal lobe and those that show univariate subsequent memory effect, have little predictive power for both forms of LTM. Interestingly, the interactions between these regions and other brain regions play a more substantial role in predicting recollection memory than recognition memory. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions associated with LTM during encoding and that such a network is separable from what supports the retention of information in working memory.

scholarly journals Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Bidhan Lamichhane ◽  
Andy G. S. Daniel ◽  
John J. Lee ◽  
Daniel S. Marcus ◽  
Joshua S. Shimony ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glioblastoma Multiforme ◽  
Functional Connectivity ◽  
Resting State ◽  
Learning Analytics ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Term Survival ◽  
Long Term Survival

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

Effect of Type of Task and Number of Inspectors on Performance of an Industrial Inspection-Type Task

1995 ◽  
Vol 37 (1) ◽  
pp. 182-192 ◽  
Author(s):  
Harold Stanislaw
Keyword(s):  
Individual Differences ◽  
Forced Choice ◽  
Sensory Memory ◽  
Long Term Memory ◽  
Rating Task ◽  
Term Memory ◽  
Industrial Inspection ◽  
Inspection Task ◽  
Practical Implications

Two hundred forty subjects working alone and in pairs performed three different versions of a task similar to industrial inspection: a rating task and spatial and temporal two-alternative forced-choice (2AFC) tasks. Performance was worse on the rating task than on the 2AFC tasks, and the spatial and temporal 2AFC tasks were performed equally well. These results could signify that performance is impaired more by demands made on long-term memory than by demands made on perception and sensory memory, or that asking subjects to compare items is fundamentally different from, and easier than, asking subjects to judge items in absolute terms. Individual differences in performance were marked, but performance was inconsistent across different versions of the inspection task. When subjects worked in pairs, performance was comparable to that obtained by requiring items to pass two inspections by individual subjects. However, a single inspection by subject pairs required less time than two inspections by individual subjects. The practical implications of these findings are discussed.

scholarly journals Effects of long-term cocaine self-administration on brain resting-state functional connectivity in nonhuman primates

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Stephen J. Kohut ◽  
Dionyssios Mintzopoulos ◽  
Brian D. Kangas ◽  
Hannah Shields ◽  
Kelly Brown ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processing ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Resting State Functional Connectivity ◽  
Self Administration ◽  
Chronic Cocaine

AbstractLong-term cocaine use is associated with a variety of neural and behavioral deficits that impact daily function. This study was conducted to examine the effects of chronic cocaine self-administration on resting-state functional connectivity of the dorsal anterior cingulate (dACC) and putamen—two brain regions involved in cognitive function and motoric behavior—identified in a whole brain analysis. Six adult male squirrel monkeys self-administered cocaine (0.32 mg/kg/inj) over 140 sessions. Six additional monkeys that had not received any drug treatment for ~1.5 years served as drug-free controls. Resting-state fMRI imaging sessions at 9.4 Tesla were conducted under isoflurane anesthesia. Functional connectivity maps were derived using seed regions placed in the left dACC or putamen. Results show that cocaine maintained robust self-administration with an average total intake of 367 mg/kg (range: 299–424 mg/kg). In the cocaine group, functional connectivity between the dACC seed and regions primarily involved in motoric behavior was weaker, whereas connectivity between the dACC seed and areas implicated in reward and cognitive processing was stronger. In the putamen seed, weaker widespread connectivity was found between the putamen and other motor regions as well as with prefrontal areas that regulate higher-order executive function; stronger connectivity was found with reward-related regions. dACC connectivity was associated with total cocaine intake. These data indicate that functional connectivity between regions involved in motor, reward, and cognitive processing differed between subjects with recent histories of cocaine self-administration and controls; in dACC, connectivity appears to be related to cumulative cocaine dosage during chronic exposure.

scholarly journals Individual Differences in Learning Efficiency

2019 ◽  
Vol 28 (6) ◽  
pp. 607-613
Author(s):  
Kathleen B. McDermott ◽  
Christopher L. Zerr
Keyword(s):  
Individual Differences ◽  
Experimental Approach ◽  
Long Term Memory ◽  
Positive Relation ◽  
Future Directions ◽  
Term Memory ◽  
Learning Efficiency ◽  
Rate Of Learning ◽  
Over Time

Most research on long-term memory uses an experimental approach whereby participants are assigned to different conditions, and condition means are the measures of interest. This approach has demonstrated repeatedly that conditions that slow the rate of learning tend to improve later retention. A neglected question is whether aggregate findings at the level of the group (i.e., slower learning tends to improve retention) translate to the level of individual people. We identify a discrepancy whereby—across people—slower learning tends to coincide with poorer memory. The positive relation between learning rate (speed of learning) and retention (amount remembered after a delay) across people is referred to as learning efficiency. A more efficient learner can acquire information faster and remember more of it over time. We discuss potential characteristics of efficient learners and consider future directions for research.

scholarly journals Discriminating Suicide Attempters and Predicting Suicide Risk Using Altered Frontolimbic Resting-State Functional Connectivity in Patients With Bipolar II Disorder

2020 ◽  
Vol 11 ◽  
Author(s):  
Rongxin Zhu ◽  
Shui Tian ◽  
Huan Wang ◽  
Haiteng Jiang ◽  
Xinyi Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Suicide Risk ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Bipolar Ii Disorder ◽  
Individual Level ◽  
Bipolar Ii ◽  
The Individual

Bipolar II disorder (BD-II) major depression episode is highly associated with suicidality, and objective neural biomarkers could be key elements to assist in early prevention and intervention. This study aimed to integrate altered brain functionality in the frontolimbic system and machine learning techniques to classify suicidal BD-II patients and predict suicidality risk at the individual level. A cohort of 169 participants were enrolled, including 43 BD-II depression patients with at least one suicide attempt during a current depressive episode (SA), 62 BD-II depression patients without a history of attempted suicide (NSA), and 64 demographically matched healthy controls (HCs). We compared resting-state functional connectivity (rsFC) in the frontolimbic system among the three groups and explored the correlation between abnormal rsFCs and the level of suicide risk (assessed using the Nurses' Global Assessment of Suicide Risk, NGASR) in SA patients. Then, we applied support vector machines (SVMs) to classify SA vs. NSA in BD-II patients and predicted the risk of suicidality. SA patients showed significantly decreased frontolimbic rsFCs compared to NSA patients. The left amygdala-right middle frontal gyrus (orbital part) rsFC was negatively correlated with NGASR in the SA group, but not the severity of depressive or anxiety symptoms. Using frontolimbic rsFCs as features, the SVMs obtained an overall 84% classification accuracy in distinguishing SA and NSA. A significant correlation was observed between the SVMs-predicted NGASR and clinical assessed NGASR (r = 0.51, p = 0.001). Our results demonstrated that decreased rsFCs in the frontolimbic system might be critical objective features of suicidality in BD-II patients, and could be useful for objective prediction of suicidality risk in individuals.

scholarly journals Thalamic Functional Connectivity during Spatial Long-Term Memory and the Role of Sex

2020 ◽  
Vol 10 (12) ◽  
pp. 898
Author(s):  
Dylan S. Spets ◽  
Scott D. Slotnick
Keyword(s):  
Sex Differences ◽  
Functional Connectivity ◽  
Region Of Interest ◽  
Brain Regions ◽  
Long Term Memory ◽  
Thalamic Nuclei ◽  
Term Memory ◽  
Correlated Activity ◽  
Abstract Shapes

The thalamus has been implicated in many cognitive processes, including long-term memory. More specifically, the anterior (AT) and mediodorsal (MD) thalamic nuclei have been associated with long-term memory. Despite extensive mapping of the anatomical connections between these nuclei and other brain regions, little is known regarding their functional connectivity during long-term memory. The current study sought to determine which brain regions are functionally connected to AT and MD during spatial long-term memory and whether sex differences exist in the patterns of connectivity. During encoding, abstract shapes were presented to the left and right of fixation. During retrieval, shapes were presented at fixation, and participants made an “old-left” or “old-right” judgment. Activations functionally connected to AT and MD existed in regions with known anatomical connections to each nucleus as well as in a broader network of long-term memory regions. Sex differences were identified in a subset of these regions. A targeted region-of-interest analysis identified anti-correlated activity between MD and the hippocampus that was specific to females, which is consistent with findings in rodents. The current results suggest that AT and MD play key roles during spatial long-term memory and suggest that these functions may be sex specific.

Working Memory: Models and Applications

2020 ◽  
Author(s):  
Stoo Sepp ◽  
Steven J. Howard ◽  
Sharon Tindall-Ford ◽  
Shirley Agostinho ◽  
Fred Paas
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Short Term Memory ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Long Term Memory ◽  
Auditory Information ◽  
Short Term ◽  
Term Memory

In 1956, Miller first reported on a capacity limitation in the amount of information the human brain can process, which was thought to be seven plus or minus two items. The system of memory used to process information for immediate use was coined “working memory” by Miller, Galanter, and Pribram in 1960. In 1968, Atkinson and Shiffrin proposed their multistore model of memory, which theorized that the memory system was separated into short-term memory, long-term memory, and the sensory register, the latter of which temporarily holds and forwards information from sensory inputs to short term-memory for processing. Baddeley and Hitch built upon the concept of multiple stores, leading to the development of the multicomponent model of working memory in 1974, which described two stores devoted to the processing of visuospatial and auditory information, both coordinated by a central executive system. Later, Cowan’s theorizing focused on attentional factors in the effortful and effortless activation and maintenance of information in working memory. In 1988, Cowan published his model—the scope and control of attention model. In contrast, since the early 2000s Engle has investigated working memory capacity through the lens of his individual differences model, which does not seek to quantify capacity in the same way as Miller or Cowan. Instead, this model describes working memory capacity as the interplay between primary memory (working memory), the control of attention, and secondary memory (long-term memory). This affords the opportunity to focus on individual differences in working memory capacity and extend theorizing beyond storage to the manipulation of complex information. These models and advancements have made significant contributions to understandings of learning and cognition, informing educational research and practice in particular. Emerging areas of inquiry include investigating use of gestures to support working memory processing, leveraging working memory measures as a means to target instructional strategies for individual learners, and working memory training. Given that working memory is still debated, and not yet fully understood, researchers continue to investigate its nature, its role in learning and development, and its implications for educational curricula, pedagogy, and practice.

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