Amygdala real-time fMRI neurofeedback upregulation in treatment resistant depression: Proof of concept and dose determination

Previous work has shown that adults suffering from major depressive disorder (MDD) can increase their amygdala reactivity while recalling positive memories via real-time neurofeedback (rt-fMRI-nf) training, which is associated with reduction in depressive symptoms. This study investigated if this intervention could also be considered for patients suffering from MDD who do not respond to standard psychological and pharmacological interventions, i.e., treatment resistant (TR-MDD). 15 participants received 5 neurofeedback sessions. Outcome measures were depressive symptoms assessed by BDI scores up to 12 weeks following acute intervention, and amygdala activity changes from initial baseline to final transfer run during neurofeedback sessions (neurofeedback success). Participants succeeded in increasing their amygdala activity. A main effect of visit on BDI scores indicated a significant reduction in depressive symptomatology. Percent signal change in the amygdala showed a learning curve during the first session only. Neurofeedback success computed by session was significantly positive only during the second session. When examining the baseline amygdala response, baseline activity stabilized/asymptoted by session 3. This proof-of-concept study suggests that only two neurofeedback sessions are necessary to enable those patients to upregulate their amygdala activity, warranting a future RCT. Over the course of the rtfMRI-nf intervention, participants also reported reduced depressive symptomatology. Clinical trial registration number: NCT03428828 on ClinicalTrials.gov.

Enhanced efficacy of CBT following augmentation with amygdala rtfMRI neurofeedback in depression

BACKGROUND

Despite cognitive behavioral therapy (CBT) being a standard treatment in major depressive disorder (MDD), nearly half of patients do not respond. As one of the predictors of CBT's efficacy is amygdala reactivity to positive information, which is often decreased in MDD, we explored whether real-time fMRI neurofeedback (rtfMRI-nf) training to increase amygdala responses during positive memory recall prior CBT would enhance its efficacy.

METHODS

In a double-blind, placebo controlled, randomized clinical trial, 35 adults with MDD received two sessions of rtfMRI-nf training to increase their amygdala (experimental group, n = 16) or parietal (control group, n = 19) responses during positive memory neurofeedback prior to receiving 10 CBT sessions. Depressive symptomatology was monitored between the rtfMRI sessions, the first three, 9th and 10th sessions of CBT and at 6 months and 1 year follow-up.

RESULTS

Participants in the experimental group showed decreased depressive symptomatology and higher remission rates at 6 months and 1 year follow-up than the control group. Analysis of CBT content highlighted that participants in the experimental group focused more on positive thinking and behaviors than the control group.

LIMITATIONS

The study was relatively small and not sufficiently powered to detect small effects.

CONCLUSIONS

CBT, when combined with amygdala neurofeedback, results in sustained clinical changes and leads to long-lasting clinical improvement, potentially by increasing focus on positive memories and cognitions.

Real-time functional magnetic resonance imaging neurofeedback training of amygdala upregulation increases affective flexibility in depression

BACKGROUND

Decreased affective flexibility is associated with depression symptoms, and it has been suggested that common interventions may target this mechanism. To explore this hypothesis, we evaluated whether real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) training to increase the amygdala responses during positive memory recall resulted in both symptom improvements, as has been observed previously, and flexibility to decrease amygdala reactivity in response to a cognitive task among patients with major depressive disorder (MDD).

METHODS

In a double-blind, placebo-controlled, randomized clinical trial, adults with MDD received 2 sessions of rtfMRI-nf training to increase their amygdala (experimental group) or parietal (control group) responses during positive autobiographical memory recall. We evaluated signal changes in the amygdala during both the positive memory neurofeedback and a subsequent counting condition.

RESULTS

We included 38 adults with MDD, including 16 in the experimental group and 22 in the control group. In the experimental group, amygdala activity increased (t > 2.01, df < 27, p < 0.05, d > 0.5) and depressive symptoms decreased (-8.57, 95 % confidence interval [CI] -15.12 to -2.59; t ₁₃ = -3.06, p = 0.009, d = 1). Amygdala activity during the count condition decreased after rtfMRI-nf (-0.16, 95 % CI -0.23 to -0.09; t ₃₉₆ = 4.73, p < 0.001, d = 0.48) and was correlated with decreased depression scores (r = 0.46, p = 0.01). We replicated previous results and extended them to show decreased amygdala reactivity to a cognitive task during which no neurofeedback was provided.

LIMITATIONS

The count condition was reported by participants as negative, but emotionality or accuracy during this condition was not assessed.

CONCLUSION

These results suggest that nominally targeting unidimensional change in neural mechanisms could have implications for bidirectional control, increasing the likely reach and explanatory framework for how common depression interventions work.Trial registration: ClinicalTrials.gov NCT02709161.

Cortico-Striatal Functional Connectivity Reflects Changes in Subjective Energy and Tiredness

Subjective feelings of energy and tiredness may reflect different neural processes. Functional connectivity (FC) was measured in 272 HealthABC participants via resting state functional MRI in striatal-associative, striatal-limbic and striatal-sensorimotor networks. Subjective energy level (scored 1-10) and tiredness (tired/not-tired) during the prior month were collected via self-report from year 2 to year 10 (mean energy follow-up=8 years, tiredness follow-up=7 years). Participants who never reported being tired during follow-up (N=119) had significantly lower FC in the striatal-limbic network (mean difference [95%CI]: -0.055 [-0.1020,-0.00879], p=0.02). Participants with stable energy level over time (N=94, defined as decline <1.0 SD below the mean) had significantly higher FC in the striatal-associative network (mean difference [95% CI]: 0.041 [0.00192,0.0807], p=0.04). Associations were similar when adjusted for brain atrophy, demographics, and education. Although based on subjective measures, the distinct spatial patterns of these associations support our hypothesis that neural basis of energy and fatigue may differ.

Prefrontal Activation is Associated With Gait Quality During an Attentional Task in Older Adults

Prefrontal cortical activation varies by walking task and is a marker of attentional demand. We compared prefrontal activation by functional near-infrared spectroscopy (fNIRS) to accelerometry-derived gait quality. We hypothesized greater activation with lower gait quality (greater step-time coefficient-of-variation, decreased cadence, smoothness, regularity, and signal variability). Participants (n=114; age 74.4±6.0 years, 59.6% female) were independently ambulating individuals &gt;64 years. Attentional (reciting every-other alphabet letter) and physical (uneven surface) challenges mimicked community mobility and provided four 15m walking conditions: even, uneven, ABC-even, and ABC-uneven. fNIRS data were referenced to quiet standing and averaged within left and right hemispheres. Gait metrics from a tri-axial accelerometer at the lower-back included cadence (steps/min), step-time coefficient-of-variation, signal variability (standard deviation), smoothness (harmonic ratio), and regularity (entropy). Associations between fNIRS and gait were quantified using Pearson correlations (α=0.05). Results were consistent across hemispheres, gait axes, and robust to adjustment for age and gait speed; we report unadjusted coefficients for left hemisphere and anterior-posterior gait direction. Greater prefrontal activation was associated with slower cadence (r=-0.220, p=0.019), lower signal variability (r=-0.228, p=0.015), and reduced smoothness (r=-0.194, p=0.039) during ABC-even. No relation was observed for step-time coefficient-of-variation or regularity. Results were similar for the ABC-uneven condition, except there was no association with gait smoothness but was with step-time coefficient-of-variation (r=0.25, p=0.007). Prefrontal activation was not correlated to gait quality in non-ABC conditions. Our findings support our hypothesis only during the ABC challenge, suggesting that older adults may rely on prefrontal activation to complete attentional but not physical challenges during gait.

Motor Skill Training Effect on Real-Time Prefrontal Cortex Activation During Walking

We aimed to test the effects of motor skill training (MST) on gait automaticity measured by changes in prefrontal cortex (PFC) activation during actual walking. We used data from a 12-week trial of older adults (mean age=75.5, 60.5% women) randomized to standard physical therapy and standard+MST in a 1:1 ratio. Functional near infrared spectroscopy (fNIRS) measured PFC activation during simple and dual task walking. We will apply linear mixed models to assess effects of task, time, and MST on PFC activation. We will compare the PFC activation 1) during dual task walking compared to simple walking; 2) across visits after intervention; and 3) between participants receiving MST compared to standard physical therapy. These results will demonstrate whether gait automaticity, as evidenced by PFC activation during walking, is affected by MST.

Mild Parkinsonian Signs Are Related to Lower Cortico-Striatal Connectivity in Executive Networks

Mild Parkinsonian signs (MPS) affect up to 24% of community-dwelling older adults. We hypothesize that MPS are associated with Parkinson’s-like alterations of functional connectivity (FC) in sensorimotor, executive, and reward cortico-striatal networks. Participants (N=266; mean age=83; 57% female) without Parkinson’s completed resting-state fMRI and Unified Parkinsonian Disease Rating Scale (UPDRS). FC between striatum and cortex was measured within each network. Logistic regression tested associations of each network’s FC with MPS (UPDRS>0), adjusted for MPS risk factors, then including white matter hyperintensities (WMH). MPS was associated with lower cortical-striatal FC in the left executive cortico-striatal network (OR [95%CI]: 0.188 [0.043,0.824]). Association survived adjusting for risk factors (0.162 [0.030,0.874]) but was attenuated after including WMH (0.209 [0.036, 1.200]). In stratified analyses, left executive cortico-striatal FC was associated with MPS only for those with higher WMH (0.077 [0.010,0.599]). Future work should examine whether higher FC protects against the influence of WMH on MPS.

Cognition and Cerebrovascular Reactivity in Midlife Women With History of Preeclampsia and Placental Evidence of Maternal Vascular Malperfusion

Background: Preeclampsia is emerging as a sex-specific risk factor for cerebral small vessel disease (SVD) and dementia, but the reason is unknown. We assessed the relationship of maternal vascular malperfusion (MVM), a marker of placental SVD, with cognition and cerebral SVD in women with and without preeclampsia. We hypothesized women with both preeclampsia and MVM would perform worst on information processing speed and executive function.

Methods: Women (n = 45; mean 10.5 years post-delivery; mean age: 41 years; 42.2% Black) were classified as preeclampsia-/MVM-, preeclampsia+/MVM-, or preeclampsia+/MVM+. Information processing speed, executive function, and memory were assessed. In a pilot sub-study of cerebrovascular reactivity (CVR; n = 22), cerebral blood flow during room-air breathing and breath-hold induced hypercapnia were obtained via arterial spin labeling MRI. Non-parametric tests and regression models were used to test associations.

Results: Between-group cognitive differences were significant for information processing speed (p = 0.02); preeclampsia+/MVM+ had the lowest scores. Cerebral blood flow increased from room-air to breath-hold, globally and in all regions in the three groups, except the preeclampsia+/MVM+ parietal region (p = 0.12). Lower parietal CVR (less change from room-air breathing to breath-holding) was correlated with poorer information processing speed (partial ρ = 0.63, p = 0.005) and executive function (ρ = 0.50, p = 0.03) independent of preeclampsia/MVM status.

Conclusion: Compared to women without preeclampsia and MVM, midlife women with both preeclampsia and MVM have worse information processing speed and may have blunted parietal CVR, an area important for information processing speed and executive function. MVM in women with preeclampsia is a promising sex-specific indicator of cerebrovascular integrity in midlife.

Brain lateralization in children with upper-limb reduction deficiency

Background

The purpose of the current study was to determine the influence of upper-limb prostheses on brain activity and gross dexterity in children with congenital unilateral upper-limb reduction deficiencies (ULD) compared to typically developing children (TD).

Methods

Five children with ULD (3 boys, 2 girls, 8.76 ± 3.37 years of age) and five age- and sex-matched TD children (3 boys, 2 girls, 8.96 ± 3.23 years of age) performed a gross manual dexterity task (Box and Block Test) while measuring brain activity (functional near-infrared spectroscopy; fNIRS).

Results

There were no significant differences (p = 0.948) in gross dexterity performance between the ULD group with prosthesis (7.23 ± 3.37 blocks per minute) and TD group with the prosthetic simulator (7.63 ± 5.61 blocks per minute). However, there was a significant (p = 0.001) difference in Laterality Index (LI) between the ULD group with prosthesis (LI = − 0.2888 ± 0.0205) and TD group with simulator (LI = 0.0504 ± 0.0296) showing in a significant ipsilateral control for the ULD group. Thus, the major finding of the present investigation was that children with ULD, unlike the control group, showed significant activation in the ipsilateral motor cortex on the non-preferred side using a prosthesis during a gross manual dexterity task.

Conclusions

This ipsilateral response may be a compensation strategy in which the existing cortical representations of the non-affected (preferred) side are been used by the affected (non-preferred) side to operate the prosthesis. This study is the first to report altered lateralization in children with ULD while using a prosthesis.

Trial registration The clinical trial (ClinicalTrial.gov ID: NCT04110730 and unique protocol ID: IRB # 614-16-FB) was registered on October 1, 2019 (https://clinicaltrials.gov/ct2/show/NCT04110730) and posted on October 1, 2019. The study start date was January 10, 2020. The first participant was enrolled on January 14, 2020, and the trial is scheduled to be completed by August 23, 2023. The trial was updated January 18, 2020 and is currently recruiting

Patterns of Prefrontal Activation and Performance During Walking Tasks Among Older Adults

Differences in prefrontal cortex (PFC) control of walking in older age likely arise from changes in neural capacity and compensation. PFC activation by changes in oxygenated hemoglobin from functional near infra-red spectroscopy was examined in 29 older adults (mean age=76). Tasks included standing with cognitive challenge and walking with and without cognitive challenge on even and uneven surfaces. Three PFC activation-performance patterns were identified using K-means clustering: 1) low activation during walking tasks and high activation during standing cognitive task, with the best performance in terms of walking speed and cognitive performance (n=10); 2) low activation on all tasks, with the lowest performance (n=15); 3) high activation during walking and low activation during cognitive, with intermediate performance (n=5). Associations of patterns with cognitive function and structural neuroimaging were explored, with results informing interpretation of functional changes of PFC during aging process, including compensatory mechanisms for primary network impairment.

The role of the right prefrontal cortex in recognition of facial emotional expressions in depressed individuals: fNIRS study

BACKGROUND

Depressed individuals often perceive neutral facial expressions as emotional. Neurobiological underpinnings of this effect remain unclear. We investigated the differences in prefrontal cortical (PFC) activation in depressed individuals vs. healthy controls (HC) during recognition of emotional and neutral facial expressions using functional near infrared spectroscopy (fNIRS).

METHOD

In Experiment 1, 33 depressed individuals and 20 HC performed the Emotion Intensity Rating task in which they rated intensity of facial emotional expressions. In Experiment 2, a different set of participants (18 depressed individuals and 16 HC) performed the same task while their PFC activation was measured using fNIRS.

RESULTS

Both experiments showed that depressed individuals were slower and less accurate in recognizing neutral, but not happy or fearful, facial emotional expressions. Experiment 2 revealed that lower accuracy for neutral facial emotional expressions was associated with lower right PFC activation in depressed individuals, but not HC. In addition, depressed individuals, compared to HC, had lower right PFC activation during recognition of happy facial expressions.

LIMITATIONS

Relatively small sample size CONCLUSIONS: Recognition of neutral facial expressions is impaired in depressed individuals. Greater impairment corresponds to lower right PFC activation during neutral face processing. Recognition of happy facial expressions is comparable for depressed individuals and HC, but the former have significantly lower right PFC activation. Taken together, these findings suggest that the ability of depressed individuals to discriminate neutral and emotional signals in the environment may be affected by aberrant functioning of right PFC.

The Effect of Prolonged Walking With Intermittent Standing on Erector Spinae and Soleus Muscle Oxygenation and Discomfort

Prolonged periods of walking have been associated with musculoskeletal discomfort and injuries. Previous research has shown that muscle fatigue is related to decreases in muscle oxygenation during short term walking. The objective of the proposed research is to determine the impact of prolonged walking with intermittent standing on musculoskeletal discomfort and muscle oxygenation measures in young adults. Nine young adults walked for a period of 2 hours. Ratings of perceived discomfort were recorded using a questionnaire. Muscle oxygenation and hemoglobin levels were collected from the lower back erector spinae and soleus muscles using near infrared spectroscopy (NIRS). Subjective discomfort significantly increased throughout the 2 hours. Prolonged walking generally induced increased oxygenation of the erector spinae and soleus across walking periods, within walking periods and across standing periods. These increases were more pronounced at the beginning of the walking session and continued through the second or third periods. Erector spinae and soleus total hemoglobin increased within walking period one and two. Only the soleus total hemoglobin significantly increased after the first walking and standing periods and during all the transitions from walking to standing. Increased oxygenation and total hemoglobin during prolonged walking with intermittent standing are likely a result of the repeated dynamic contractions and exercise-induced blood volume expansion. Increased discomfort was found; however, this was not explained by detrimental changes in oxygenation or total hemoglobin.

Investigation of the sensitivity of functional near-infrared spectroscopy brain imaging to anatomical variations in 5- to 11-year-old children

Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that uses scalp-placed light sensors to measure evoked changes in cerebral blood oxygenation. The portability, low overhead cost, and ability to use this technology under a wide range of experimental environments make fNIRS well-suited for studies involving infants and children. However, since fNIRS does not directly provide anatomical or structural information, these measurements may be sensitive to individual or group level differences associated with variations in head size, depth of the brain from the scalp, or other anatomical factors affecting the penetration of light into the head. This information is generally not available in pediatric populations, which are often the target of study for fNIRS. Anatomical magnetic resonance imaging information from 90 school-age children (5 to 11 years old) was used to quantify the expected effect on fNIRS measures of variations in cerebral and extracerebral structure. Monte Carlo simulations of light transport in tissue were used to estimate differential and partial optical pathlengths at 690, 780, 808, 830, and 850 nm and their variations with age, sex, and head size. This work provides look-up tables of these values and general guidance for future investigations using fNIRS sans anatomical information in this child population.

Comparison of group-level, source localized activity for simultaneous functional near-infrared spectroscopy-magnetoencephalography and simultaneous fNIRS-fMRI during parametric median nerve stimulation

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique, which uses light to measure changes in cerebral blood oxygenation through sensors placed on the surface of the scalp. We recorded concurrent fNIRS with magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) in order to investigate the group-level correspondence of these measures with source-localized fNIRS estimates. Healthy participants took part in both a concurrent fNIRS-MEG and fNIRS-fMRI neuroimaging session during two somatosensory stimulation tasks, a blocked design median nerve localizer and parametric pulsed-pair median nerve stimulation using interpulse intervals from 100 to 500 ms. We found the spatial correlation for estimated activation patterns from the somatosensory task was [Formula: see text], 0.57, and [Formula: see text] and the amplitude correlation was [Formula: see text], 0.52, and [Formula: see text] for fMRI-MEG, fMRI-fNIRS oxy-hemoglobin, and fMRI-fNIRS deoxy-hemoglobin signals, respectively. Taken together, these results show good correspondence among the fMRI, fNIRS, and MEG with the great majority of the difference across modalities being driven by lower sensitivity for deeper brain sources in MEG and fNIRS. These results provide an important validation of source-localized fNIRS in the context of concurrent multimodal imaging for future studies of the relationship between physiological effects in the human brain.

Dynamic filtering improves attentional state prediction with fNIRS

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%).

An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks

BACKGROUND

Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks.

NEW METHOD

In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a "learning network" would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity.

RESULTS

Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition.

COMPARISON WITH EXISTING METHODS

Most of the current literature does not examine stationarity prior to processing.

CONCLUSIONS

The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data.

Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging method that uses light to record regional changes in cerebral blood flow in the cortex during activation. fNIRS uses portable wearable sensors to allow measurements of brain activation during tasking. In this study, fNIRS was used to investigate how the brain processes information from multiple sensory modalities during dynamic posturography. Fifteen healthy volunteers (9M/6F; ages 28+/-9 yrs) participated in the posturography study while undergoing fNIRS brain imaging. Four standard conditions from the sensory organization test (SOT) were performed and a bilateral fNIRS probe was used to examine the cortical brain responses from the frontal, temporal, and parietal brain regions. We found that there was bilateral activation in the temporal-parietal areas (superior temporal gyrus, STG, and supramarginal gyrus, SMG) when both vision and proprioceptive information were degraded; forcing reliance on primarily vestibular information in the control of balance. This is consistent with previous reports of the role of these regions in vestibular control and demonstrates the potential utility of fNIRS in the study of cortical control of vestibular function during standing balance tasks.

Measurement of brain activation during an upright stepping reaction task using functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technology that uses light to measure changes in cortical hemoglobin concentrations. FNIRS measurements are recorded through fiber optic cables, which allow the participant to wear the fNIRS sensors while standing upright. Thus, fNIRS technology is well suited to study cortical brain activity during upright balance, stepping, and gait tasks. In this study, fNIRS was used to measure changes in brain activation from the frontal, motor, and premotor brain regions during an upright step task that required subjects to step laterally in response to visual cues that required executive function control. We hypothesized that cognitive processing during complex stepping cues would elicit brain activation of the frontal cortex in areas involved in cognition. Our results show increased prefrontal activation associated with the processing of the stepping cues. Moreover, these results demonstrate the potential to use fNIRS to investigate cognitive processing during cognitively demanding balance and gait studies.

A random-effects model for group-level analysis of diffuse optical brain imaging

Diffuse optical imaging is a non-invasive technique for measuring changes in blood oxygenation in the brain. This technique is based on the temporally and spatially resolved recording of optical absorption in tissue within the near-infrared range of light. Optical imaging can be used to study functional brain activity similar to functional MRI. However, group level comparisons of brain activity from diffuse optical data are difficult due to registration of optical sensors between subjects. In addition, optical signals are sensitive to inter-subject differences in cranial anatomy and the specific arrangement of optical sensors relative to the underlying functional region. These factors can give rise to partial volume errors and loss of sensitivity and therefore must be accounted for in combining data from multiple subjects. In this work, we describe an image reconstruction approach using a parametric Bayesian model that simultaneously reconstructs group-level images of brain activity in the context of a random-effects analysis. Using this model, we demonstrate that localization accuracy and the statistical effects size of group-level reconstructions can be improved when compared to individualized reconstructions. In this model, we use the Restricted Maximum Likelihood (ReML) method to optimize a Bayesian random-effects model.

Hierarchical Bayesian regularization of reconstructions for diffuse optical tomography using multiple priors

Diffuse optical tomography (DOT) is a non-invasive brain imaging technique that uses low-levels of near-infrared light to measure optical absorption changes due to regional blood flow and blood oxygen saturation in the brain. By arranging light sources and detectors in a grid over the surface of the scalp, DOT studies attempt to spatially localize changes in oxy- and deoxy-hemoglobin in the brain that result from evoked brain activity during functional experiments. However, the reconstruction of accurate spatial images of hemoglobin changes from DOT data is an ill-posed linearized inverse problem, which requires model regularization to yield appropriate solutions. In this work, we describe and demonstrate the application of a parametric restricted maximum likelihood method (ReML) to incorporate multiple statistical priors into the recovery of optical images. This work is based on similar methods that have been applied to the inverse problem for magnetoencephalography (MEG). Herein, we discuss the adaptation of this model to DOT and demonstrate that this approach provides a means to objectively incorporate reconstruction constraints and demonstrate this approach through a series of simulated numerical examples.

Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model

Near-infrared spectroscopy is a non-invasive neuroimaging method which uses light to measure changes in cerebral blood oxygenation associated with brain activity. In this work, we demonstrate the ability to record and analyze images of brain activity in real-time using a 16-channel continuous wave optical NIRS system. We propose a novel real-time analysis framework using an adaptive Kalman filter and a state-space model based on a canonical general linear model of brain activity. We show that our adaptive model has the ability to estimate single-trial brain activity events as we apply this method to track and classify experimental data acquired during an alternating bilateral self-paced finger tapping task.

Simultaneous recording of task-induced changes in blood oxygenation, volume, and flow using diffuse optical imaging and arterial spin-labeling MRI

Increased neural activity in brain tissue is accompanied by an array of supporting physiological processes, including increases in blood flow and the rates at which glucose and oxygen are consumed. These responses lead to secondary effects such as alterations in blood oxygenation and blood volume, and are ultimately the primary determinants of the amplitude and temporal signature of the blood oxygenation level-dependent (BOLD) signal used prevalently to map brain function. We have performed experiments using a combination of optical and MRI-based imaging methods to develop a more comprehensive picture of the physiological events accompanying activation of primary motor cortex during a finger apposition task. Temporal profiles for changes in tissue hemoglobin concentrations were qualitatively similar to those observed for MRI-based flow and oxygenation signals. Quantitative analysis of these signals revealed peak changes of +16 +/- 2% for HbO, -13 +/- 2% for HbR, +8 +/- 3% for total Hb, +83 +/- 9% for cerebral blood flow, and +1.4 +/- 0.1% for the BOLD MRI signal. A mass balance model was used to estimate the change in rate of oxidative metabolism implied by the optical and flow measurements, leading to a computed value of +47 +/- 5%. It should be noted that the optical and MRI observations may in general reflect changes over different volumes of tissue. The ratio of fractional changes in oxidative metabolism to fractional change in blood flow was found to be 0.56 +/- 0.08, in general agreement with previous studies of flow-metabolism coupling.