Recent works shed light on the neural correlates of true and false recognition and the influence of time of day on cognitive performance. The current study aimed to investigate the modulation of the false memory formation by the time of day using a non-linear correlation analysis originally designed for fMRI resting-state data. Fifty-four young and healthy participants (32 females, mean age: 24.17 ± 3.56 y.o.) performed in MR scanner the modified Deese-Roediger-McDermott paradigm in short-term memory during one session in the morning and another in the evening. Subjects' responses were modeled with a general linear model, which includes as a predictor the non-linear correlations of regional BOLD activity with the stimuli, separately for encoding and retrieval phases. The results show the dependence of the non-linear correlations measures with the time of day and the type of the probe. In addition, the results indicate differences in the correlations measures with hippocampal regions between positive and lure probes. Besides confirming previous results on the influence of time-of-day on cognitive performance, the study demonstrates the effectiveness of the non-linear correlation analysis method for the characterization of fMRI task paradigms.

In functional magnetic resonance imaging (fMRI) analysis, many studies have been conducted on inter-subject variability as well as intra-subject reproducibility. These studies indicate that fMRI could have unique characteristics for individuals. In this study, we hypothesized that the dynamic information during 1 min of fMRI was unique and repetitive enough for each subject, so we applied long short-term memory (LSTM) using initial time points of dynamic resting-state fMRI for individual identification. Siamese network is used to obtain robust individual identification performance without additional learning on a new dataset. In particular, by adding a new structure called region of interest-wise average pooling (RAP), individual identification performance could be improved, and key intrinsic connectivity networks (ICNs) for individual identification were also identified. The average performance of individual identification was 97.88% using the test dataset in eightfold cross-validation analysis. Through the visualization of features learned by Siamese LSTM with RAP, ICNs spanning the parietal region were observed as the key ICNs in identifying individuals. These results suggest the key ICNs in fMRI could represent individual uniqueness.

Worldwide, and especially in Western civilizations, most of the staple diets contain high amounts of fat and refined carbohydrates, leading to an increasing number of obese individuals. In addition to inducing metabolic disorders, energy dense food intake has been suggested to impair brain functions such as cognition and mood control. Here we demonstrate an impaired memory function already 3 days after the start of a high-fat diet (HFD) exposure, and depressive-like behavior, in the tail suspension test, after 5 days. These changes were followed by reduced synaptic density, changes in mitochondrial function and astrocyte activation in the hippocampus. Preceding or coinciding with the behavioral changes, we found an induction of the proinflammatory cytokines TNF-α and IL-6 and an increased permeability of the blood-brain barrier (BBB), in the hippocampus. Finally, in mice treated with a TNF-α inhibitor, the behavioral and BBB alterations caused by HFD-feeding were mitigated suggesting that inflammatory signaling was critical for the changes. In summary, our findings suggest that HFD rapidly triggers hippocampal dysfunction associated with BBB disruption and neuroinflammation, promoting a progressive breakdown of synaptic and metabolic function. In addition to elucidating the link between diet and cognitive function, our results might be relevant for the comprehension of the neurodegenerative process.

Spatial working memory is a kind of short-term memory that allows temporarily storing and manipulating spatial information. Evidence suggests that spatial working memory is processed through three distinctive phases: Encoding, maintenance, and retrieval. Though the medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) are involved in memory retrieval, how the functional interactions and information transfer between mPFC and MD remains largely unclear.

Objective: To explore quantitative measurements of the visual attention and neuroelectrophysiological relevance of memory deficits in temporal lobe epilepsy (TLE) by eye tracking and electroencephalography (EEG). Methods: Thirty-four TLE patients and twenty-eight healthy controls were invited to complete neurobehavioral assessments, cognitive oculomotor tasks, and 24-h video EEG (VEEG) recordings using an automated computer-based memory assessment platform with an eye tracker. Visit counts, visit time, and time of first fixation on areas of interest (AOIs) were recorded and analyzed in combination with interictal epileptic discharge (IED) characteristics from the bilateral temporal lobes. Results: The TLE patients had significantly worse Wechsler Digit Span scores [F(1, 58) = 7.49, p = 0.008]. In the Short-Term Memory Game with eye tracking, TLE patients took a longer time to find the memorized items [F(1, 57) = 17.30, p < 0.001]. They had longer first fixation [F(1, 57) = 4.06, p = 0.049] and more visit counts [F(1, 57) = 7.58, p = 0.008] on the target during the recall. Furthermore, the performance of the patients in the Digit Span task was negatively correlated with the total number of IEDs [r(28) = -0.463, p = 0.013] and the number of spikes per sleep cycle [r(28) = -0.420, p = 0.026]. Conclusion: Eye tracking appears to be a quantitative, objective measure of memory evaluation, demonstrating memory retrieval deficits but preserved visual attention in TLE patients. Nocturnal temporal lobe IEDs are closely associated with memory performance, which might be the electrophysiological mechanism for memory impairment in TLE.

The role of Histamine H3 receptors (H3Rs) in memory, and the prospective of H3R antagonists in pharmacological control of neurodegenerative disorders, e.g., Alzheimer disease (AD) is well-accepted. For that reason, the procognitive effects of the H3R antagonist DL77 on cognitive impairments induced with MK801 were tested in an inhibitory passive avoidance paradigm (PAP) and novel object recognition (NOR) task in adult male rats, using donepezil (DOZ) as a standard drug. Acute systemic pretreatment with DL77 (2.5, 5, and 10 mg/kg, i.p.) significantly ameliorated memory deficits induced with MK801 in PAP (all P < 0.05, n = 7). The ameliorative effect of most promising dose of DL77 (5 mg/kg, i.p.) was reversed when rats were co-injected with the H3R agonist R-(α)-methylhistamine (RAMH, 10 mg/kg, i.p.) (p = 0.701 for MK801-amnesic group vs. MK801+DL77+RAMH group, n = 6). In the NOR paradigm, DL77 (5 mg/kg, i.p.) counteracted long-term memory (LTM) deficits induced with MK801 (P < 0.05, n = 6-8), and the DL77-provided effect was similar to that of DOZ (p = 0.788, n = 6-8), and was reversed when rats were co-injected with RAMH (10 mg/kg, i.p.) (p = 0.877, n = 6, as compared to the (MK801)-amnesic group). However, DL77 (5 mg/kg, i.p.) did not alter short-term memory (STM) impairment in NOR test (p = 0.772, n = 6-8, as compared to (MK801)-amnesic group). Moreover, DL77 (5 mg/kg) failed to modify anxiety and locomotor behaviors of animals innate to elevated-plus maze (EPM) (p = 0.67 for percentage of time spent exploring the open arms, p = 0.52 for number of entries into the open arms, p = 0.76 for percentage of entries into the open arms, and p = 0.73 number of closed arm entries as compared to saline-treated groups, all n = 6), demonstrating that the procognitive effects observed in PAP or NOR tests were unconnected to alterations in emotions or in natural locomotion of tested animals. These results signify the potential involvement of H3Rs in modulating neurotransmitters related to neurodegenerative disorders, e.g., AD.

Objective: Patients with Parkinson's disease have prospective memory impairments. However, little is known about distinct phases of prospective memory in these patients. This study was designed to elucidate the specific phase(s) of prospective memory that are impaired in patients with Parkinson's disease. Methods: The study included 31 Parkinson's disease patients and 27 healthy controls. The four phases of prospective memory (intention formation, retention, initiation, and execution) were examined in a complex prospective memory task. In this task, the participants were asked to form a sophisticated plan for performing six subtasks to obtain the highest score, and then execute the plan following a cue embedded in a questionnaire. Global cognitive function and relevant cognitive abilities, including attention, short-term memory, working memory, and inhibition, were also evaluated during the retention phase of the prospective memory task. Results: Intention formation was impaired in Parkinson's disease patients (p < 0.001 vs. healthy controls). This impairment could not be attributed to deficits in other cognitive functions. The score of intention execution was also lower in Parkinson's disease patients (p = 0.004 vs. healthy controls). Such a difference was related to working memory deficits in Parkinson's disease. The intention retention and initiation were intact in Parkinson's disease patients. The score of intention execution correlated negatively with disease severity and disease duration. Conclusions: Prospective memory in Parkinson's disease patients is impaired at the phase of intention formation. The worsening performance of intention execution in Parkinson's disease may be related to working memory deficits. In addition, prospective memory impairment might progress with increasing disease duration and severity.

Gulf War Illness (GWI), also called Chronic Multisymptom Illness (CMI), is a multi-faceted condition that plagues an estimated 250,000 Gulf War (GW) veterans. Symptoms of GWI/CMI include fatigue, pain, and cognitive dysfunction. We previously reported that 12% of a convenience sample of middle aged (median age 52 years) GW veterans met criteria for mild cognitive impairment (MCI), a clinical syndrome most prevalent in older adults (e.g., ≥70 years). The current study sought to replicate and extend this finding.

Space exploration has advanced substantially over recent decades and plans to increase the duration of deep space missions are in preparation. One of the primary health concerns is potential damage to the central nervous system (CNS), resulting in loss of cognitive abilities and function. The majority of ground-based research on space radiation-induced health risks has been conducted using single particle simulations, which do not effectively model real-world scenarios. Thus, to improve the safety of space missions, we must expand our understanding of the effects of simulated galactic cosmic rays (GCRs) on the CNS. To assess the effects of low-dose GCR, we subjected 6-month-old male BALB/c mice to 50 cGy 5-beam simplified GCR spectrum (1H, 28Si, 4He, 16O, and 56Fe) whole-body irradiation at the NASA Space Radiation Laboratory. Animals were tested for cognitive performance with Y-maze and Morris water maze tests 3 months after irradiation. Irradiated animals had impaired short-term memory and lacked spatial memory retention on day 5 of the probe trial. Glial cell analysis by flow cytometry showed no significant changes in oligodendrocytes, astrocytes, microglia or neural precursor cells (NPC's) between the sham group and GCR group. Bone marrow cytogenetic data showed a significant increase in the frequency of chromosomal aberrations after GCR exposure. Finally, tandem mass tag proteomics identified 3,639 proteins, 113 of which were differentially expressed when comparing sham versus GCR exposure (fold change > 1.5; p < 0.05). Our data suggest exposure to low-dose GCR induces cognitive deficits by impairing short-term memory and spatial memory retention.

Individual differences and age-related normal and pathological changes in mental abilities require the use of cognitive screening and assessment tools. However, simultaneously occurring deficits in sensory processing, whose prevalence increases especially in old age, may negatively impact cognitive-test performance and thus result in an overestimation of cognitive decline. This hypothesis was tested using an impairment-simulation approach. Young normal-hearing university students performed three memory tasks, using auditorily presented speech stimuli that were either unprocessed or processed to mimic some of the perceptual consequences of age-related hearing loss (ARHL). Both short-term-memory and working-memory capacities were significantly lower in the simulated-hearing-loss condition, despite good intelligibility of the test stimuli. The findings are consistent with the notion that, in case of ARHL, the perceptual processing of auditory stimuli used in cognitive assessments requires additional (cognitive) resources that cannot be used toward the execution of the cognitive task itself. Researchers and clinicians would be well advised to consider sensory impairments as a confounding variable when administering cognitive tasks and interpreting their results.

This paper introduces a heterogeneous spiking neural network (H-SNN) as a novel, feedforward SNN structure capable of learning complex spatiotemporal patterns with spike-timing-dependent plasticity (STDP) based unsupervised training. Within H-SNN, hierarchical spatial and temporal patterns are constructed with convolution connections and memory pathways containing spiking neurons with different dynamics. We demonstrate analytically the formation of long and short term memory in H-SNN and distinct response functions of memory pathways. In simulation, the network is tested on visual input of moving objects to simultaneously predict for object class and motion dynamics. Results show that H-SNN achieves prediction accuracy on similar or higher level than supervised deep neural networks (DNN). Compared to SNN trained with back-propagation, H-SNN effectively utilizes STDP to learn spatiotemporal patterns that have better generalizability to unknown motion and/or object classes encountered during inference. In addition, the improved performance is achieved with 6x fewer parameters than complex DNNs, showing H-SNN as an efficient approach for applications with constrained computation resources.

Previous studies have used task-related fMRI to investigate the neural basis of pitch identification (PI), but no study has examined the associations between resting-state functional connectivity (RSFC) and PI ability. Using a large sample of Chinese non-musicians (N = 320, with 56 having prior musical training), the current study examined the associations among musical training, PI ability, and RSFC. Results showed that musical training was associated with increased RSFC within the networks for multiple cognitive functions (such as vision, phonology, semantics, auditory encoding, and executive functions). PI ability was associated with RSFC with regions for perceptual and auditory encoding for participants with musical training, and with RSFC with regions for short-term memory, semantics, and phonology for participants without musical training.

The mechanisms underlying intracerebral hemorrhage (ICH)-related cognitive impairment (CI) remain unclear. Long-term structural and functional changes were investigated in the brains of healthy male and female Wistar rats after experimental ICH. Following double injection of autologous blood, rats underwent short-term (onset, 3 and 7 days) and long-term (3 and 6 months) radiological assessment and behavioral tests exploring spontaneous locomotion, anxiety-like behavior and working memory, spatial recognition memory and visual recognition memory. Volumetric and metabolic changes in brain areas were examined by 7Tesla-MRI and [18F] FDG-PET, respectively. Brain connectomic disorders and maladaptive processes were seeked through brain metabolic connectivity analysis and atrophy-related network analysis. From an initial hematoma mean volume of 23.35 ± 9.50 mm3, we found early spontaneous locomotor recovery and significant spontaneous blood resorption (≈ 40% of the initial lesion) from days 0 to 7. After 3 and 6 months, ICH rats exhibited CI in several domains as compared to the sham group (working memory: 58.1 ± 1.2 vs. 70.7 ± 1.2%, p < 0.001; spatial recognition memory: 48.7 ± 1.9 vs. 64 ± 1.8%, p < 0.001 and visual recognition memory: 0.14 ± 0.05 vs. 0.33 ± 0.04, p = 0.013, in female only). Rats that experienced ICH had remote and concomitant cerebral atrophy and hypometabolism of ipsilateral striatum, thalamus, limbic system and cortical areas (temporal and parietal lobes). Interestingly, both structural and metabolic deterioration was found in the limbic system connected to the affected site, but remotely from the initial insult. On the other hand, increased activity and functional connectivity occurred in the contralateral hemisphere. These connectomics results showed that both maladaptative and compensation processes coexist in the rat brain following ICH, even at young age and in a disease-free setting. These radiological findings deepen our understanding of ICH-related CI and may serve as biomarkers in the view of future therapeutic intervention.

Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have revealed brain activations time-locked to spindles. Yet, the functional significance of these spindle-related brain activations is not understood. EEG studies have shown that inter-individual differences in the electrophysiological characteristics of spindles (e.g., density, amplitude, duration) are highly correlated with "Reasoning" abilities (i.e., "fluid intelligence"; problem solving skills, the ability to employ logic, identify complex patterns), but not short-term memory (STM) or verbal abilities. Spindle-dependent reactivation of brain areas recruited during new learning suggests night-to-night variations reflect offline memory processing. However, the functional significance of stable, trait-like inter-individual differences in brain activations recruited during spindle events is unknown. Using EEG-fMRI sleep recordings, we found that a subset of brain activations time-locked to spindles were specifically related to Reasoning abilities but were unrelated to STM or verbal abilities. Thus, suggesting that individuals with higher fluid intelligence have greater activation of brain regions recruited during spontaneous spindle events. This may serve as a first step to further understand the function of sleep spindles and the brain activity which supports the capacity for Reasoning.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that is the most common cause of dementia. Optogenetics uses a combination of genetic engineering and light to activate or inhibit specific neurons in the brain. Objective: The objective of the study was to examine the effect of activation of glutamatergic neurons in the hippocampus of mice injected with Aβ1-42 on memory function and biomarkers of neuroinflammation and neuroprotection in the brain to elucidate the clinical utility of optogenetic neuromodulation in AD. Methods: AAV5-CaMKII-channelrhodopsin-2 (CHR2)-mCherry (Aβ-CHR2 mice) or AAV5-CaMKII-mCherry (Aβ-non-CHR2 mice) was injected into the dentate gyrus (DG) of the bilateral hippocampus of an Aβ1-42-injected mouse model of AD. The novel object recognition test was used to investigate working memory (M1), short-term memory (M2), and long-term memory (M3) after Aβ1-42 injection. Hippocampus tissues were collected for immunohistochemical analysis. Results: Compared to controls, M1 and M2 were significantly higher in Aβ-CHR2 mice, but there was no significant difference in M3; NeuN and synapsin expression were significantly increased in the DG of Aβ-CHR2 mice, but not in CA1, CA3, the subventricular zone (SVZ), or the entorhinal cortex (ENT); GluR2 and IL-10 expressions were significantly increased, and GFAP expression was significantly decreased, in CA1, CA3, the DG, and the SVZ of Aβ-CHR2 mice, but not in the ENT. Conclusion: Activation of glutamatergic neurons by optogenetics in the bilateral DG of an Aβ-injected mouse model of AD improved M1 and M2, but not M3. A single-target optogenetics strategy has spatial limitations; therefore, a multiple targeted optogenetics approach to AD therapy should be explored.

The experimental study of neural networks requires simultaneous measurements of a massive number of neurons, while monitoring properties of the connectivity, synaptic strengths and delays. Current technological barriers make such a mission unachievable. In addition, as a result of the enormous number of required measurements, the estimated network parameters would differ from the original ones. Here we present a versatile experimental technique, which enables the study of recurrent neural networks activity while being capable of dictating the network connectivity and synaptic strengths. This method is based on the observation that the response of neurons depends solely on their recent stimulations, a short-term memory. It allows a long-term scheme of stimulation and recording of a single neuron, to mimic simultaneous activity measurements of neurons in a recurrent network. Utilization of this technique demonstrates the spontaneous emergence of cooperative synchronous oscillations, in particular the coexistence of fast γ and slow δ oscillations, and opens the horizon for the experimental study of other cooperative phenomena within large-scale neural networks.

Most magneto- and electroencephalography (M/EEG) based source estimation techniques derive their estimates sample wise, independently across time. However, neuronal assemblies are intricately interconnected, constraining the temporal evolution of neural activity that is detected by MEG and EEG; the observed neural currents must thus be highly context dependent. Here, we use a network of Long Short-Term Memory (LSTM) cells where the input is a sequence of past source estimates and the output is a prediction of the following estimate. This prediction is then used to correct the estimate. In this study, we applied this technique on noise-normalized minimum norm estimates (MNE). Because the correction is found by using past activity (context), we call this implementation Contextual MNE (CMNE), although this technique can be used in conjunction with any source estimation method. We test CMNE on simulated epileptiform activity and recorded auditory steady state response (ASSR) data, showing that the CMNE estimates exhibit a higher degree of spatial fidelity than the unfiltered estimates in the tested cases.

Executive function (EF) plays vital roles in our everyday adaptation to the ever-changing environment. However, limited existing studies have linked EF to the resting-state brain activity. The functional connectivity in the resting state between the sub-regions of the brain can reveal the intrinsic neural mechanisms involved in cognitive processing of EF without disturbance from external stimuli. The present study investigated the relations between the behavioral executive function (EF) scores and the resting-state functional network topological properties in the Prefrontal Cortex (PFC). We constructed complex brain functional networks in the PFC from 90 healthy young adults using functional near infrared spectroscopy (fNIRS). We calculated the correlations between the typical network topological properties (regional topological properties and global topological properties) and the scores of both the Total EF and components of EF measured by computer-based Cambridge Neuropsychological Test Automated Battery (CANTAB). We found that the Total EF scores were positively correlated with regional properties in the right dorsal superior frontal gyrus (SFG), whereas the opposite pattern was found in the right triangular inferior frontal gyrus (IFG). Different EF components were related to different regional properties in various PFC areas, such as planning in the right middle frontal gyrus (MFG), working memory mainly in the right MFG and triangular IFG, short-term memory in the left dorsal SFG, and task switch in the right MFG. In contrast, there were no significant findings for global topological properties. Our findings suggested that the PFC plays an important role in individuals' behavioral performance in the executive function tasks. Further, the resting-state functional network can reveal the intrinsic neural mechanisms involved in behavioral EF abilities.

Sleep stage classification is an open challenge in the field of sleep research. Considering the relatively small size of datasets used by previous studies, in this paper we used the Sleep Heart Health Study dataset from the National Sleep Research Resource database. A long short-term memory (LSTM) network using a time-frequency spectra of several consecutive 30 s time points as an input was used to perform the sleep stage classification. Four classical convolutional neural networks (CNNs) using a time-frequency spectra of a single 30 s time point as an input were used for comparison. Results showed that, when considering the temporal information within the time-frequency spectrum of a single 30 s time point, the LSTM network had a better classification performance than the CNNs. Moreover, when additional temporal information was taken into consideration, the classification performance of the LSTM network gradually increased. It reached its peak when temporal information from three consecutive 30 s time points was considered, with a classification accuracy of 87.4% and a Cohen's Kappa coefficient of 0.8216. Compared with CNNs, our results indicate that for sleep stage classification, the temporal information within the data or the features extracted from the data should be considered. LSTM networks take this temporal information into account, and thus, may be more suitable for sleep stage classification.

The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size, and complex temporo-spatial dependency structure of these data. Even further, DL models often act as black boxes, impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framework, which utilizes long short-term memory (LSTM) based DL models to analyze whole-brain functional Magnetic Resonance Imaging (fMRI) data. To decode a cognitive state (e.g., seeing the image of a house), DeepLight separates an fMRI volume into a sequence of axial brain slices, which is then sequentially processed by an LSTM. To maintain interpretability, DeepLight adapts the layer-wise relevance propagation (LRP) technique. Thereby, decomposing its decoding decision into the contributions of the single input voxels to this decision. Importantly, the decomposition is performed on the level of single fMRI volumes, enabling DeepLight to study the associations between cognitive state and brain activity on several levels of data granularity, from the level of the group down to the level of single time points. To demonstrate the versatility of DeepLight, we apply it to a large fMRI dataset of the Human Connectome Project. We show that DeepLight outperforms conventional approaches of uni- and multivariate fMRI analysis in decoding the cognitive states and in identifying the physiologically appropriate brain regions associated with these states. We further demonstrate DeepLight's ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples.