Sensors and Systems for Monitoring Mental Fatigue: A systematic review

Mental fatigue is a leading cause of motor vehicle accidents, medical errors, loss of workplace productivity, and student disengagements in e-learning environment. Development of sensors and systems that can reliably track mental fatigue can prevent accidents, reduce errors, and help increase workplace productivity. This review provides a critical summary of theoretical models of mental fatigue, a description of key enabling sensor technologies, and a systematic review of recent studies using biosensor-based systems for tracking mental fatigue in humans. We conducted a systematic search and review of recent literature which focused on detection and tracking of mental fatigue in humans. The search yielded 57 studies (N=1082), majority of which used electroencephalography (EEG) based sensors for tracking mental fatigue. We found that EEG-based sensors can provide a moderate to good sensitivity for fatigue detection. Notably, we found no incremental benefit of using high-density EEG sensors for application in mental fatigue detection. Given the findings, we provide a critical discussion on the integration of wearable EEG and ambient sensors in the context of achieving real-world monitoring. Future work required to advance and adapt the technologies toward widespread deployment of wearable sensors and systems for fatigue monitoring in semi-autonomous and autonomous industries is examined.Comment: 19 Pages, 3 Figure

Detection of microsleeps from the eeg via optimized classification techniques.

Microsleeps are complete breaks in responsiveness for 0.5–15 s. They can lead to multiple fatalities in certain occupational fields (e.g., transportation and military) due to the need in such occupations for extended and continuous vigilance. Therefore, an automated microsleep detection system may assist in the reduction of poor job performance and occupational fatalities. An EEG-based microsleep detector offers advantages over a videobased microsleep detector, including speed and temporal resolution. A series of software modules were implemented to examine different feature sets to determine the optimal circumstances for automated EEG-based microsleep detection. The microsleep detection system was organized in a similar manner to an EEG-based brain-computer interface (BCI). EEG data underwent baseline removal and filtering to remove overhead noise. Following this, feature extraction generated spectral features based upon an estimate of the power spectrum or its logarithmic transform. Following this, feature selection/reduction (FS/R) was used to select the most relevant information across all the spectral features. A trained classifier was then tested on data from a subject it had not seen before. In certain cases, an ensemble of classifiers was used instead of a single classifier. The performance measures from all cases were then averaged together in leave-one-out crossvalidation (LOOCV). Sets of artificial data were generated to test a prototype EEG-based microsleep detection system, consisting of a combination of EEG and 2-s bursts of 15 Hz sinusoids of varied signal-to-noise ratios (SNRs) ranging from 16 down to 0.03. The balance between events and non-events was varied between evenly balanced and highly imbalanced (e.g., events occurring only 2% of the time). Features were spectral estimates of various EEG bands (e.g., alpha band power) or ratios between them. A total of 34 features for each of the 16 channels yielded a total of 544 features. Five minutes of EEG from eight subjects were used in the generation of the dummy data, and each subject yielded a matrix of 300 observations of 544 features. Datasets from two prior microsleep studies were employed after validating the system on the artificial data. The first, Study A (N = 8), had 16 channels sampled at 256 Hz from two 1-hour sessions per subject and the second, Study C (N = 10), had one 50-min session with 30-62 channels per subject sampled at 250 Hz. A vector of 34 spectral features from each channel was concatenated into a feature vector for each 2-s interval, with each interval having a 1-s overlap with the prior one. In both cases, microsleeps had been identified via a combination of video recording and performance on a continuous tracking task. Study A provided four datasets to compare effects of various preprocessing techniques on performance: (1) Study A bipolar EEG with Independent Component Analysis (ICA) preprocessing and artefact pruning (total automated rejection of artefact-containing epochs) and logarithmic transforms of the spectral features (SABIL); (2) Study A bipolar EEG with ICA-based eye blink removal and artefact removal with pruning of epochs with major artefacts, and linear spectral features (SABIS); (3) Study A referential EEG unprocessed by ICA with spectral features (SARUS); and (4) Study A bipolar EEG unprocessed by ICA with spectral features (SABUS). The second study had one primary feature set, the Study C referential EEG ICA preprocessed spectral feature (SCRIS) variant. LOOCV was evaluated based on the phi correlation coefficient. After replicating prior work, several FS/R and classifier structures were investigated with both the artificially balanced and unbalanced data. Feature selection/reduction methods included principal component analysis (PCA), common spatial patterns (CSP), projection to latent structures (PLS), a new method based on average distance between events and nonevents (ADEN), ADEN normalized with a z-score transform (ADENZ), genetic algorithms in concert with ADEN (GADEN), and genetic algorithms in concert with ADENZ (GADENZ). Several pattern recognition algorithms were investigated: linear discriminant analysis (LDA), radial basis functions (RBFs), and Support Vector Machines with Gaussian (SVMG) and polynomial (SVMP) kernels. Classifier structures examined included single classifiers, bagging, boosting, stacking, and adaptive boosting (AdaBoost). The highest LOOCV results on artificial data (SNR = 0.3) corresponded to GADEN with 10 features and a single LDA classifier with a mean phi value of 0.96. Of the four Study A datasets, PCA with 150 features and a stacking ensemble achieved the highest mean phi of 0.40 with the SABIL feature set, and ADEN with 20 features with a single LDA classifier achieved the highest mean phi of 0.10 with Study C. Other machine-learning methodologies, such as training on artificially balanced data, decreasing the training size, within-subject training and testing, and randomly mixed data from across subjects, were also examined. Training on artificially balanced data did not improve performance. An issue found by performing within-subject training and testing was that, for certain subjects, a classifier trained on one-half of the subject’s data and then tested on the other half was that classifier performance dropped to random guessing. The low phi values on within-subject tests occurred independently of the feature selection/reduction method explored. As such, performance of a standard LOOCV was often dependent on whether a particular testing subject had a low (< 0.15) within-subjects mean phi correlation coefficient. Training on only the higher mean phi values did not boost performance. Additional tests found correlations (r = 0.57, p = 0.003 for Study A and r = 0.67, p 0.15) and longer mean microsleep durations. Other individual subject characteristics, such as number of microsleeps and subject age, did not have significant differences. The primary findings highlighted the strengths and limitations of supervised feature selection and linear classifiers trained upon highly variable between-subject features across two studies. Findings suggested that a classifier performs best when individuals have high mean microsleep durations. On the configurations investigated, preprocessing factors, such as ICA preprocessing, feature extraction method, and artefact pruning, affected the performance more than changing specific module configurations. No significant differences between the SABIL features and the lower performing Study A feature sets were found due to overlapping ranges of performance (p = 0.15). The findings suggest that the investigated techniques plateaued in performance on the Study A data, reaching a point of diminishing returns without fundamentally changing the nature of the classification problem. The different number of channels of varying quality across all subjects in Study C rendered microsleep classification extremely difficult, but even a linear classifier can properly generalize if exposed to a large enough variety of data from across the entire set. Many of the techniques explored are also relevant to other fields, such as braincomputer interface (BCI) and machine learning

Lapses in Responsiveness: Characteristics and Detection from the EEG

Performance lapses in occupations where public safety is paramount can have disastrous consequences, resulting in accidents with multiple fatalities. Drowsy individuals performing an active task, like driving, often cycle rapidly between periods of wake and sleep, as exhibited by cyclical variation in both EEG power spectra and task performance measures. The aim of this project was to identify reliable physiological cues indicative of lapses, related to behavioural microsleep episodes, from the EEG, which could in turn be used to develop a real-time lapse detection (or better still, prediction) system. Additionally, the project also sought to achieve an increased understanding of the characteristics of lapses in responsiveness in normal subjects. A study was conducted to determine EEG and/or EOG cues (if any) that expert raters use to detect lapses that occur during a psychomotor vigilance task (PVT), with the subsequent goal of using these cues to design an automated system. A previously-collected dataset comprising physiological and performance data of 10 air traffic controllers (ATCs) was used. Analysis showed that the experts were unable to detect the vast majority of lapses based on EEG and EOG cues. This suggested that, unlike automated sleep staging, an automated lapse detection system needed to identify features not generally visible in the EEG. Limitations in the ATC dataset led to a study where more comprehensive physiological and performance data were collected from normal subjects. Fifteen non-sleep-deprived male volunteers aged 18-36 years were recruited. All performed a 1-D continuous pursuit visuomotor tracking task for 1 hour during each of two sessions that occurred between 1 and 7 weeks apart. A video camera was used to record head and facial expressions of the subject. EEG was recorded from electrodes at 16 scalp locations according to the 10-20 system at 256 Hz. Vertical and horizontal EOG was also recorded. All experimental sessions were held between 12:30 and 17:00 hours. Subjects were asked to refrain from consuming stimulants or depressants, for 4 h prior to each session. Rate and duration were estimated for lapses identified by a tracking flat spot and/or video sleep. Fourteen of the 15 subjects had one or more lapses, with an overall rate of 39.3 ± 12.9 lapses per hour (mean ± SE) and a lapse duration of 3.4 ± 0.5 s. The study also showed that lapsing and tracking error increased during the first 30 or so min of a 1-h session, then decreased during the remaining time, despite the absence of external temporal cues. EEG spectral power was found to be higher during lapses in the delta, theta, and alpha bands, and lower in the beta, gamma, and higher bands, but correlations between changes in EEG power and lapses were low. Thus, complete lapses in responsiveness are a frequent phenomenon in normal subjects - even when not sleep-deprived - undertaking an extended, monotonous, continuous visuomotor task. This is the first study to investigate and report on the characteristics of complete lapses of responsiveness during a continuous tracking task in non-sleep-deprived subjects. The extent to which non-sleep-deprived subjects experience complete lapses in responsiveness during normal working hours was unexpected. Such findings will be of major concern to individuals and companies in various transport sectors. Models based on EEG power spectral features, such as power in the traditional bands and ratios between bands, were developed to detect the change of brain state during behavioural microsleeps. Several other techniques including spectral coherence and asymmetry, fractal dimension, approximate entropy, and Lempel-Ziv (LZ) complexity were also used to form detection models. Following the removal of eye blink artifacts from the EEG, the signal was transformed into z-scores relative to the baseline of the signal. An epoch length of 2 s and an overlap of 1 s (50%) between successive epochs were used for all signal processing algorithms. Principal component analysis was used to reduce redundancy in the features extracted from the 16 EEG derivations. Linear discriminant analysis was used to form individual classification models capable of detecting lapses using data from each subject. The overall detection model was formed by combining the outputs of the individual models using stacked generalization with constrained least-squares fitting used to determine the optimal meta-learner weights of the stacked system. The performance of the lapse detector was measured both in terms of its ability to detect lapse state (in 1-s epochs) and lapse events. Best performance in lapse state detection was achieved using the detector based on spectral power (SP) features (mean correlation of φ = 0.39 ± 0.06). Lapse event detection performance using SP features was moderate at best (sensitivity = 73.5%, selectivity = 25.5%). LZ complexity feature-based detector showed the highest performance (φ = 0.28 ± 0.06) out of the 3 non-linear feature-based detectors. The SP+LZ feature-based model had no improvement in performance over the detector based on SP alone, suggesting that LZ features contributed no additional information. Alpha power contributed the most to the overall SP-based detection model. Analysis showed that the lapse detection model was detecting phasic, rather than tonic, changes in the level of drowsiness. The performance of these EEG-based lapse detection systems is modest. Further research is needed to develop more sensitive methods to extract cues from the EEG leading to devices capable of detecting and/or predicting lapses

SLEEPING WHILE AWAKE: A NEUROPHYSIOLOGICAL INVESTIGATION ON SLEEP DURING WAKEFULNESS.

Il sonno e la veglia vengono comunemente considerati come due stati distinti. L\u2019alternanza tra essi, la cui presenza \ue8 stata dimostrata in ogni specie animale studiata fino ad oggi, sembra essere una delle caratteristiche che definisce la nostra vita. Allo stesso tempo, per\uf2, le scoperte portate alla luce negli ultimi decenni hanno offuscato i confini tra questi due stati. I meccanismi del sonno hanno sempre affascinato i neurofisiologi, che infatti, nell\u2019ultimo secolo, li hanno caratterizzati in dettaglio: ora sappiamo che all\u2019attivit\ue0 del sonno sottost\ue0 una specifica attivit\ue0 neuronale chiamata slow oscillation. La slow oscillation, che \ue8 costituita da (ancora una volta) un\u2019alternanza tra periodi di attivit\ue0 e periodi di iperpolarizzazione e silenzio neuronale (OFF-periods), \ue8 la modalit\ue0 base di attivazione del cervello dormiente. Questa alternanza \ue8 dovuta alla tendenza dei neuroni surante lo stato di sonno, di passare ad un periodo silente dopo un\u2019attivazione iniziale, una tendenza a cui viene dato il nome di bistabilit\ue0 neuronale. Molti studi hanno dimostrato come la bistabilit\ue0 neuronale tipica del sonno ed i relativi OFF-periods, possano accadere anche durante la veglia in particolari condizioni patologiche, nelle transizioni del sonno e durante le deprivazioni di sonno. Per questo motivo, se accettassimo che la bistabilit\ue0 neuronale e gli OFF-periods rappresentino una caratteristica fondamentale del sonno, allora dovremmo ammettere che stiamo assistendo ad un cambio di paradigma: da una prospettiva neurofisiologica il sonno pu\uf2 intrudere nella veglia. In questa tesi ho analizzato i nuovi -fluidi- confini tra sonno e veglia e le possibili implicazioni di questi nel problema della persistenza personale attraverso il tempo. Inoltre, ho studiato le implicazioni cliniche dell\u2019intrusione di sonno nella veglia in pazienti con lesioni cerebrali focali di natura ischemica. In particolare, i miei obiettivi sono stati: 1) Dimostrare come la bistabilit\ue0 neuronale possa essere responsabile della perdita di funzione nei pazienti affetti da ischemia cerebrale e come questo potrebbe avere implicazioni nello studio della patofisiologia dell\u2019ischemia cerebrale e nella sua terapia; 2) Stabilire le basi per un modello di sonno locale presente nella vita di tutti i giorni: la sensazione di sonnolenza. Infatti, essa potrebbe riflettere la presenza di porzioni di corteccia in stato di sonno, ma durante lo stato di veglia; 3) Difendere il criterio biologico di identit\ue0, che troverebbe nell\u2019attivit\ue0 cerebrale la continuit\ue0 necessaria al mantenimento della nostra identit\ue0 nel tempo.Sleep and wakefulness are considered two mutually exclusive states. The alternation between those two states seems to be a defining characteristic of our life, a ubiquitous phenomenon demonstrated in every animal species investigated so far. However, during the last decade, advances in neurophysiology have blurred the boundaries between those states. The mechanisms of sleep have always intrigued neurophysiologists and great advances have been made over the last century in understanding them: we now know that the defining characteristic underlying sleep activity is a specific pattern of neuronal activity, namely the slow oscillation. The slow oscillation, which is characterized by the periodic alternation between periods of activity (ON-periods) and periods of hyperpolarization and neuronal silence (OFF-periods) is the default mode of activity of the sleeping cortex. This alternation is due to the tendency of neurons to fall into a silent period after an initial activation; such tendency is known as \u201cbistability\u201d. There is accumulating evidence that sleep-like bistability, and the ensuing OFF-periods, may occur locally in the awake human brain in some pathological conditions, in sleep transition, as well as after sleep deprivation. Therefore, to the extent that bistability and OFF periods represents the basic neuronal features of sleep, a paradigm shift is in place: from a neurophysiological perspective sleep can intrude into wakefulness. In this thesis, I explore the fluid boundaries between sleep and wakefulness and investigate their possible implications on the problem of personal persistence over time. Moreover, I study the clinical implications of the intrusion of sleep into wakefulness in patients with focal brain injury due to stroke. Specifically, I aim to: 1) show how the sleep-like bistability can be responsible for the loss of function in stroke patients. This may have implications for understanding the pathophysiology of stroke and helping to foster recovery; 2) establish the basis for a model of local sleep that might be present in the everyday life, id est the sensation of sleepiness. Indeed, sleepiness could reflect islands of sleep during wakefulness; 3) advocate the biological criterion of identity, in which the continuity necessary for maintaining ourselves over time could be represented by never resting activity in the brain

Eeg Mechanism Interaction To Evaluate Vehicle’s Driver Microsleep

Microsleep or more commonly known as momentary uncontrollable fall asleep in a very short period of time usually occurs between one second to fifteen seconds. In Malaysia, one of the factors that contribute to accidents is due to the microsleep factor when the driver is driving without them being aware. This factor also often occurs when driving in a tired state and traveling too long distances. Weather factors can also contribute to microsleep. Therefore, in this research, a system has been developed to detect frequency waves from the brain based on signals from electroencephalogram (EEG) electrodes to prevent drivers from experiencing microsleep and getting involved in accidents. To conduct this research, five subjects of different ages and gender were selected to collect their brainwave data using the NeuroSky Mindwave Mobile Headset device and the EegID Record application in two different situations, namely by driving the simulation in a challenging condition for 30 minutes and the second situation is by driving the simulation in a relaxed condition for 30 minutes. In addition, the use of MATLAB in this research is to pre-process the wave signal to remove unwanted noise interference. Then, a bandpass filter is used to classify and separate the signal into Theta, Alpha, and Beta waves. These three waves will be analyzed and studied based on the age and gender differences of the subjects. After the spectrum of the wave is drawn to trigger the alarm system and the steering vibration motor if microsleep is detected for some period of one to 3 seconds

Modelling of the switching behavior of functional connectivity microstates (FCμstates) as a novel biomarker for mild cognitive impairment

It is evident the need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI). MCI patients have a high risk of developing Alzheimer’s disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings about the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys)function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings. The activity of different brain rhythms {δ, θ, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine ninety anatomical regions of interest (ROIs). A dynamic functional connectivity graph (DFCG) was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and also cross-frequency coupling pairs (28). We analysed DFCG profiles of neuromagnetic resting state recordings of 18 Mild Cognitive Impairment (MCI) patients and 20 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments that further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. Estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (nμstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation. We succeeded a high classification accuracy on the blind dataset (85 %) which further supports the proposed Markovian modelling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could manipulate properly the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI

Wide-field optical imaging of neurological disorders and sleep in mice

Neuroimaging has revolutionized the way in which we understand the hierarchical organization of the amazingly complex, interconnected human brain. Neuroimaging techniques, like functional magnetic resonance imaging (fMRI), have provided high quality structural and functional data, providing multiple in-depth analyses and biomarkers of disease processes. In animal models, mechanistic studies can uncover root pathologies that aren’t explorable in humans. In mice, brain functional connectivity (FC) can be measured via Optical Intrinsic Signal (OIS) imaging – a modality that measures vascular reactivity as a surrogate for neural activity via quantification of fluctuations in oxygenated-hemoglobin (similar to the blood oxygen level dependent (BOLD) signal used in fMRI). Another advantage of optical neuroimaging in mice is the expression of genetically encoded calcium indicators (GECIs), which provide cell-specific and network-level functional imaging of brain activity at speeds up to at least 4Hz. Imaging in higher frequency bands (compared to \u3c0.2Hz in fMRI or other hemoglobin-based imaging modalities) allows for resolution of neural specific phenomena on the order of milliseconds, such as the global ∼1Hz slow oscillation that is characteristic of anesthesia and non-rapid eye movement (NREM) sleep. We imaged mice expressing the GECI GCaMP6 in excitatory neurons while awake, in NREM (verified by EEG), or under ketamine/xylazine (K/X) or Dexmedetomidine (Dex) anesthesia and reconcile discrepancies between activity dynamics observed with hemoglobin vs. calcium (GCaMP6) imaging. Alterations in correlation structure were most obvious in delta band calcium NREM and anesthesia data, resulting in maps with large regions of polarized positive and negative correlations covering the field-of-view (FOV). We use principal component analysis (PCA) to provide evidence that the slow oscillation superimposes on FC rather than replaces FC patterns typical of the alert state. While consciousness state can oscillate on the order of seconds, many studies of disease processes are most informative across a longer period of time. Surgical preparations coupled with optical imaging allow for longitudinal experiments on varying timescales. For example, sequalae of subarachnoid hemorrhage (SAH) include vasospasm, microvessel thrombi, and other delayed cerebral ischemic (DCI) events around 3 days post SAH. These DCI events have been shown to coincide with up-regulation of the neuroprotective peptide Sirtuin1 (SIRT1), using an endovascular perforation mouse model. Here, we display global FC disruption caused by SAH and DCI events in parallel with behavioral deterioration. Normal brain connectivity and behavior was maintained during SAH and DCI via two different treatments targeting SIRT1 activation. SIRT1-specific (resveratrol) and non-specific (hypoxic conditioning) treatments both protected against the FC deficits induced by SAH and DCI, with the latter providing the largest protective effect. This indicates that conditioning-based strategies targeting SIRT1-directed mechanisms provide multifaceted neurovascular protection in experimental SAH – data that further supports the overarching hypothesis that conditioning- based therapy is a powerful approach with great potential for improving patient outcome after aneurysmal SAH. Studies involving focal injury (e.g., stroke, SAH) usually exhibit functional deficits surrounding the injured tissue, however, it is less clear how diffuse processes, such as novel models of acute septic encephalopathy (i.e., Delirium), and encephalitis caused by Zika virus infection, alter brain dynamics. Septic encephalopathy leads to major and costly burdens for a large percentage of admitted hospital patients. Elderly patients are at an increased risk, especially those with dementia. Current treatments are aimed at sedation to combat mental status changes and are not aimed at the underlying cause of encephalopathy. Indeed, the underlying pathology linking together peripheral infection and altered neural function has not been established, largely because good, acutely accessible readouts of encephalopathy in animal models do not exist. In-depth behavioral testing in animals lasts multiple days, outlasting the time frame of acute encephalopathy. Here, we propose optical fluorescent imaging of neural FC as a readout of encephalopathy in a mouse model of acute sepsis. Imaging and basic behavioral assessment was performed at baseline, Hr8, Hr24, and Hr72 following injection of either lipopolysaccharide (LPS) or phosphate buffered saline (PBS). Neural FC strength decreased at Hr8 and returned to baseline by Hr72 in somatosensory and parietal cortical regions. Additionally, neural fluctuations transiently declined at Hr8 and returned to baseline by Hr72. Both FC strength and neural fluctuation tone correlated with behavioral neuroscore indicating this imaging methodology is a sensitive and acute readout of encephalopathy. Zika virus (ZIKV) emerged as a prominent global health concern due to the severe neurologic injury in infants born to adults who had ZIKV infection during pregnancy. However, neurologic manifestations in healthy adults were subsequently reported during Zika pandemics in South America and Southeast Asia. In this population, infection can result in severe cases of encephalitis and have lasting impacts on cognition, and learning and memory, even after recovery from acute infection. Recent studies have uncovered extensive ZIKV- related neural apoptosis within the trisynaptic circuit involving the entorhinal cortex, the cornu ammonis, and the dentate gyrus of the hippocampus in adult mice. However, there are many contributing regions and circuits involved in cognition and learning and memory outside of this trisynaptic circuit. Communication within the cortex and between the cortex and hippocampus is necessary for a variety of neurological processes, such as performing cognitive tasks or for memory consolidation during sleep. Here, we investigate cortical networks and connectivity utilizing wide-field optical fluorescence imaging. We demonstrate that functional deficits congregate in regions of cortex that are highly communicative with hippocampus, such as somatosensory and retrosplenial cortices. Further, we prove that these functional imaging deficits are correlated with other metrics of disease severity, such as encephalitis score and increased delta power, providing a potentially useful clinical biomarker of disease. Finally, these imaging deficits resolve after recovery from acute infection. While optical methods have obvious advantages when used to study animal models, the technique is relatively novel (compared to fMRI) therefore, there are many avenues for data processing algorithms to improve. Similar to fMRI, historically, optical methods use a remarkably simple bivariate Pearson-based approach to mapping FC, leading to quick and easy-to-interpret models of brain networks but also susceptibility to global sources of variance (e.g., motion, Mayer waves). Previously, we demonstrated the binarizing effect of the slow oscillation on FC during NREM and K/X anesthesia. While PCA effectively removed the slow oscillation, it is reasonable to assume that a biological process cannot be completely explained in algebraically orthogonal components. Therefore, we pioneer a multivariate approach to imputing individual neural networks from spontaneous neuroimaging data in mice in an effort to map connectivity with less susceptibility to confounding variance. Calcium dynamics in all brain pixels are holistically weighted via support vector regression to predict activity in a region of interest (ROI). This approach yielded remarkably high prediction accuracy, suggesting the optimized pixel weights represent multivariate functional connectivity (MFC) strength with the ROI. Additionally, MFC maps were largely impervious to the slow oscillation. Moreover, MFC maps more closely aligned with anatomical connectivity as modeled through axonal projection images, than FC maps. Lastly, MFC analysis provided a more powerful connectivity deficit detection following stroke compared to standard FC. These results show that MFC has several performance and conceptual advantages over standard FC and should be considered more broadly within the FC analysis community. Further, with study of diffuse processes (e.g., LPS and ZIKV infection), statistical developments are crucial to solve the multiple comparisons problem when examining all cortical regions within the FOV. Therefore, part of this thesis focuses on the development of a streamlined, open source, user friendly data processing toolbox that contains multiple statistical approaches to make the aforementioned studies possible. Together, the following presents the multiple ways wide-field optical imaging can be used to learn more about the brain’s functional architecture in health and disease