Functional near-infrared spectroscopy: A novel tool for detecting consciousness after acute severe brain injury.

Recent advancements in functional neuroimaging have demonstrated that some unresponsive patients in the intensive care unit retain a level of consciousness that is inconsistent with their behavioral diagnosis of awareness. Functional near-infrared spectroscopy (fNIRS) is a portable optical neuroimaging method that can be used to measure neural activity with good temporal and spatial resolution. However, the reliability of fNIRS for detecting the neural correlates of consciousness remains to be established. In a series of studies, we evaluated whether fNIRS can record sensory, perceptual, and command-driven neural processing in healthy participants and in behaviorally nonresponsive patients. At the individual healthy subject level, we demonstrate that fNIRS can detect commonly studied resting state networks, sensorimotor processing, speech-specific auditory processing, and volitional command-driven brain activity to a motor imagery task. We then tested fNIRS with three acutely brain injured patients and found that one could willfully modulate their brain activity when instructed to imagine playing a game of tennis-providing evidence of preserved consciousness despite no observable behavioral signs of awareness. The successful application of fNIRS for detecting preserved awareness among behaviorally nonresponsive patients highlights its potential as a valuable tool for uncovering hidden cognitive states in critical care settings.

Different activation in dorsolateral prefrontal cortex between anxious depression and non-anxious depression during an autobiographical memory task: A fNIRS study.

OBJECTIVE: Using functional near-infrared spectroscopy (fNIRS) previous studies have found that activation differences in the dorsolateral prefrontal cortex (DLPFC) during an autobiographical memory task (AMT) under the condition of different emotional valences may be neurophysiological markers of depression and different depression subtypes. Additionally, compared with non-anxious depression, anxious depression presents abnormal hemodynamic activation in the DLPFC. This study aimed to use fNIRS to investigate hemodynamic activation in the DLPFC of depression patients with and without anxiety during AMT triggered by different emotional valence stimuli. METHODS: We recruited 194 patients with depression (91 with non-anxious depression, 103 with anxious depression) and 110 healthy controls from Chinese college students. A 53-channel fNIRS was used to detect cerebral hemodynamic differences in the three groups during AMT. RESULTS: The results showed that: (1) the activation of oxy-Hb in the left DLPFC was significantly higher under positive emotional valence than under negative emotional valence for healthy controls and patients with non-anxious depression, while there was no significant difference between positive and negative emotional valence observed in response to anxious depression; and (2) Oxy-Hb activation under negative emotional valence was significantly higher in the anxious depression group than in the non-anxious depression group. CONCLUSIONS: This study revealed that the hemodynamic hyperactivation of negative emotional valence in the left DLPFC may be due to the neurophysiological differences between anxious and non-anxious patients with depression.

Discriminative analysis of schizophrenia and major depressive disorder using fNIRS.

BACKGROUND: Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions. METHODS: A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists. RESULTS: Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively. CONCLUSION: This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.

Hyperscanning to explore social interaction among autistic minds.

Hyperscanning - the monitoring of brain activity of two or more people simultaneously - has emerged to be a popular tool for assessing neural features of social interaction. This perspective article focuses on hyperscanning studies that use functional near-infrared spectroscopy (fNIRS), a technique that is very conducive to studies requiring naturalistic paradigms. In particular, we are interested in neural features that are related to social interaction deficits among individuals with autism spectrum disorders (ASD). This population has received relatively little attention in research using neuroimaging hyperscanning techniques, compared to neurotypical individuals. The study is outlined as follows. First, we summarize the findings about brain-behavior connections related to autism from previously published fNIRS hyperscanning studies. Then, we propose a preliminary theoretical framework of inter-brain coherence (IBC) with testable hypotheses concerning this population. Finally, we provide two examples of areas of inquiry in which studies could be particularly relevant for social-emotional/behavioral development for autistic children, focusing on intergenerational relationships in family units and learning in classroom settings in mainstream schools.

Lexical Morphology as a Source of Risk and Resilience for Learning to Read With Dyslexia: An fNIRS Investigation.

PURPOSE: We examined the neurocognitive bases of lexical morphology in children of varied reading abilities to understand the role of meaning-based skills in learning to read with dyslexia. METHOD: Children completed auditory morphological and phonological awareness tasks during functional near-infrared spectroscopy neuroimaging. We first examined the relation between lexical morphology and phonological processes in typically developing readers (Study 1, N = 66, Mage = 8.39), followed by a more focal inquiry into lexical morphology processes in dyslexia (Study 2, N = 50, Mage = 8.62). RESULTS: Typical readers exhibited stronger engagement of language neurocircuitry during the morphology task relative to the phonology task, suggesting that morphological analyses involve synthesizing multiple components of sublexical processing. This effect was stronger for more analytically complex derivational affixes (like + ly) than more semantically transparent free base morphemes (snow + man). In contrast, children with dyslexia exhibited stronger activation during the free base condition relative to derivational affix condition. Taken together, the findings suggest that although children with dyslexia may struggle with derivational morphology, they may also use free base morphemes' semantic information to boost word recognition. CONCLUSION: This study informs literacy theories by identifying an interaction between reading ability, word structure, and how the developing brain learns to recognize words in speech and print. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25944949.

Analyzing fractal dimension in electroconvulsive therapy: Unraveling complexity in structural and functional neuroimaging.

BACKGROUND: Numerous studies show that electroconvulsive therapy (ECT) induces hippocampal neuroplasticity, but findings are inconsistent regarding its clinical relevance. This study aims to investigate ECT-induced plasticity of anterior and posterior hippocampi using mathematical complexity measures in neuroimaging, namely Higuchi's fractal dimension (HFD) for fMRI time series and the fractal dimension of cortical morphology (FD-CM). Furthermore, we explore the potential of these complexity measures to predict ECT treatment response. METHODS: Twenty patients with a current depressive episode (16 with major depressive disorder and 4 with bipolar disorder) underwent MRI-scans before and after an ECT-series. Twenty healthy controls matched for age and sex were also scanned twice for comparison purposes. Resting-state fMRI data were processed, and HFD was computed for anterior and posterior hippocampi. Group-by-time effects for HFD in anterior and posterior hippocampi were calculated and correlations between HFD changes and improvement in depression severity were examined. For FD-CM analyses, we preprocessed structural MRI with CAT12's surface-based methods. We explored group-by-time effects for FD-CM and the predictive value of baseline HFD and FD-CM for treatment outcome. RESULTS: Patients exhibited a significant increase in bilateral hippocampal HFD from baseline to follow-up scans. Right anterior hippocampal HFD increase was associated with reductions in depression severity. We found no group differences and group-by-time effects in FD-CM. After applying a whole-brain regression analysis, we found that baseline FD-CM in the left temporal pole predicted reduction of overall depression severity after ECT. Baseline hippocampal HFD did not predict treatment outcome. CONCLUSION: This study suggests that HFD and FD-CM are promising imaging markers to investigate ECT-induced neuroplasticity associated with treatment response.

Lights on music cognition: A systematic and critical review of fNIRS applications and future perspectives.

Research investigating the neural processes related to music perception and production constitutes a well-established field within the cognitive neurosciences. While most neuroimaging tools have limitations in studying the complexity of musical experiences, functional Near-Infrared Spectroscopy (fNIRS) represents a promising, relatively new tool for studying music processes in both laboratory and ecological settings, which is also suitable for both typical and pathological populations across development. Here we systematically review fNIRS studies on music cognition, highlighting prospects and potentialities. We also include an overview of fNIRS basic theory, together with a brief comparison to characteristics of other neuroimaging tools. Fifty-nine studies meeting inclusion criteria (i.e., using fNIRS with music as the primary stimulus) are presented across five thematic sections. Critical discussion of methodology leads us to propose guidelines of good practices aiming for robust signal analyses and reproducibility. A continuously updated world map is proposed, including basic information from studies meeting the inclusion criteria. It provides an organized, accessible, and updatable reference database, which could serve as a catalyst for future collaborations within the community. In conclusion, fNIRS shows potential for investigating cognitive processes in music, particularly in ecological contexts and with special populations, aligning with current research priorities in music cognition.

Study of a PST-trained voice-enabled artificial intelligence counselor for adults with emotional distress (SPEAC-2): Design and methods.

BACKGROUND: Novel and scalable psychotherapies are urgently needed to address the depression and anxiety epidemic. Leveraging artificial intelligence (AI), a voice-based virtual coach named Lumen was developed to deliver problem solving treatment (PST). The first pilot trial showed promising changes in cognitive control measured by functional neuroimaging and improvements in depression and anxiety symptoms. METHODS: To further validate Lumen in a 3-arm randomized clinical trial, 200 participants with mild-to-moderate depression and/or anxiety will be randomly assigned in a 2:1:1 ratio to receive Lumen-coached PST, human-coached PST as active treatment comparison, or a waitlist control condition where participants can receive Lumen after the trial period. Participants will be assessed at baseline and 18 weeks. The primary aim is to confirm neural target engagement by testing whether compared with waitlist controls, Lumen participants will show significantly greater improvements from baseline to 18 weeks in the a priori neural target for cognitive control, right dorsal lateral prefrontal cortex engaged by the go/nogo task (primary superiority hypothesis). A secondary hypothesis will test whether compared with human-coached PST participants, Lumen participants will show equivalent improvements (i.e., noninferiority) in the same neural target from baseline to 18 weeks. The second aim is to examine (1) treatment effects on depression and anxiety symptoms, psychosocial functioning, and quality of life outcomes, and (2) relationships of neural target engagement to these patient-reported outcomes. CONCLUSIONS: This study offers potential to improve the reach and impact of psychotherapy, mitigating access, cost, and stigma barriers for people with depression and/or anxiety. CLINICALTRIALS: gov #: NCT05603923.

Using dynamic spatio-temporal graph pooling network for identifying autism spectrum disorders in spontaneous functional infrared spectral sequence signals.

BACKGROUND: Autism classification work on fNIRS data using dynamic graph networks. Explore the impact of the dynamic connection relationship between brain channels on ASD, and compare the brain channel connection diagrams of ASD and TD to explore potential factors that influence the development of autism. METHOD: Using dynamic graph construction to mine the dynamic relationships of fNIRS data, obtain spatio-temporal correlations through dynamic feature extraction, and improve the information extraction capabilities of the network through spatio-temporal graph pooling to achieve classification of ASD. RESULT: A classification effect with an accuracy of 97.2% was achieved using a short sequence of 1.75s. The results showed that the dynamic connections of channel 5 and 19, channel 12 and 25, and channel 7 and 34 have a greater impact on the classification of autism. Comparison with previously used method(s): Compared with previous deep learning models, our model achieves efficient classification using short-term fNIRS data of 1.75s, and analyzes the impact of dynamic connections on classification through dynamic graphs. CONCLUSION: Using Dynamic Spatio-Temporal Graph Pooled Neural Networks (DSTGPN), dynamic connectivity between brain channels was found to have an impact on the classification of autism. By modeling the brain channel relationship maps of ASD and TD, hyperlink clusters were found to exist on the brain channel connections of ASD.

Functional neuroimaging and behavioral correlates of multisite tDCS as an add-on to language training in a person with post-stroke non-fluent aphasia: a year-long case study.

Mary, who experienced non-fluent aphasia as a result of an ischemic stroke, received 10 years of personalized language training (LT), resulting in transient enhancements in speech and comprehension. To enhance these effects, multisite transcranial Direct Current Stimulation (tDCS) was added to her LT regimen for 15 sessions. Assessment using the Reliable Change Index showed that this combination improved her left inferior frontal connectivity and speech production for two months and significantly improved comprehension after one month. The results indicate that using multisite transcranial direct current stimulation (tDCS) can improve the effectiveness of language therapy (LT) for individuals with non-fluent aphasia.

Overlapping neural correlates underpin theory of mind and semantic cognition: Evidence from a meta-analysis of 344 functional neuroimaging studies.

Key unanswered questions for cognitive neuroscience include whether social cognition is underpinned by specialised brain regions and to what extent it simultaneously depends on more domain-general systems. Until we glean a better understanding of the full set of contributions made by various systems, theories of social cognition will remain fundamentally limited. In the present study, we evaluate a recent proposal that semantic cognition plays a crucial role in supporting social cognition. While previous brain-based investigations have focused on dissociating these two systems, our primary aim was to assess the degree to which the neural correlates are overlapping, particularly within two key regions, the anterior temporal lobe (ATL) and the temporoparietal junction (TPJ). We focus on activation associated with theory of mind (ToM) and adopt a meta-analytic activation likelihood approach to synthesise a large set of functional neuroimaging studies and compare their results with studies of semantic cognition. As a key consideration, we sought to account for methodological differences across the two sets of studies, including the fact that ToM studies tend to use nonverbal stimuli while the semantics literature is dominated by language-based tasks. Overall, we observed consistent overlap between the two sets of brain regions, especially in the ATL and TPJ. This supports the claim that tasks involving ToM draw upon more general semantic retrieval processes. We also identified activation specific to ToM in the right TPJ, bilateral anterior mPFC, and right precuneus. This is consistent with the view that, nested amongst more domain-general systems, there is specialised circuitry that is tuned to social processes.

Disentangling the effects of near-infrared light stimulation and exercise on cognitive function in fNIRS studies.

Functional near-infrared spectroscopy (fNIRS) studies often aim to measure changes in the brain's hemodynamic response in relation to a specific intervention. We recently showed how a fNIRS device could induce photobiomodulatory effects on cognition by using its near-infrared (NIR) light. However, so far, fNIRS research has overlooked the stimulatory potential intrinsic to this technique. The work by Kuwamizu et al. (2023) on pupil dynamics during exercise is no exception. Here, we suggest a fix to their experimental design, which could be taken into account in other fNIRS studies, to guarantee an adequate level of control for possible unconsidered photobiomodulatory effects.

Spontaneous eye movements reflect the representational geometries of conceptual spaces.

Functional neuroimaging studies indicate that the human brain can represent concepts and their relational structure in memory using coding schemes typical of spatial navigation. However, whether we can read out the internal representational geometries of conceptual spaces solely from human behavior remains unclear. Here, we report that the relational structure between concepts in memory might be reflected in spontaneous eye movements during verbal fluency tasks: When we asked participants to randomly generate numbers, their eye movements correlated with distances along the left-to-right one-dimensional geometry of the number space (mental number line), while they scaled with distance along the ring-like two-dimensional geometry of the color space (color wheel) when they randomly generated color names. Moreover, when participants randomly produced animal names, eye movements correlated with low-dimensional similarity in word frequencies. These results suggest that the representational geometries used to internally organize conceptual spaces might be read out from gaze behavior.

Correlation between subcutaneous adipose tissue of the head and body mass index in children and young adults aged 8-19 years: implications for functional neuroimaging.

High body mass index (BMI) is presumed to signify high amounts of fat (subcutaneous adipose tissue) distributed across the body. High amounts of fat co-occurring with increased BMI has been cited as a potential neuroimaging barrier. Presence of increased fat may result in high electrical impedance and increased light diffusion-resulting in low signal to noise ratios during electroencepholography (EEG), functional near-infrared spectroscopy (fNIRS), and transcranial direct current stimulation (tDCS) measurements. Examining if subcutaneous fat in the head increases with respect to total body fat percentage and BMI in school-aged children and adolescents is an essential next step in developing possible mathematical corrections for neuroimaging modalities. We hypothesized that percentage of subcutaneous adipose tissue in the head region would increase with respect to both total body fat percentage and BMI. Increased subcutaneous head fat percentage was associated with a positive linear relationship with BMI and a quadratic relationship with total body fat. The data indicate that participant age, sex, and adiposity should be considered in the development of model corrections for neuroimaging signal processing in school-aged children and adolescents. Strength of regression coefficients in our models differed from those in adults, indicating that age-specific models should be utilized.

Brexanolone Treatment in a Real-World Patient Population: A Case Series and Pilot Feasibility Study of Precision Neuroimaging.

PURPOSE/BACKGROUND: Brexanolone is approved for postpartum depression (PPD) by the United States Food and Drug Administration. Brexanolone has outperformed placebo in clinical trials, but less is known about the efficacy in real-world patients with complex social and medical histories. Furthermore, the impact of brexanolone on large-scale brain systems such as changes in functional connectivity (FC) is unknown. METHODS/PROCEDURES: We tracked changes in depressive symptoms across a diverse group of patients who received brexanolone at a large medical center. Edinburgh Postnatal Depression Scale (EPDS) scores were collected through chart review for 17 patients immediately prior to infusion through approximately 1 year postinfusion. In 2 participants, we performed precision functional neuroimaging (pfMRI), including before and after treatment in 1 patient. pfMRI collects many hours of data in individuals for precision medicine applications and was performed to assess the feasibility of investigating changes in FC with brexanolone. FINDINGS/RESULTS: The mean EPDS score immediately postinfusion was significantly lower than the mean preinfusion score (mean change [95% CI]: 10.76 [7.11-14.40], t (15) = 6.29, P < 0.0001). The mean EPDS score stayed significantly lower at 1 week (mean difference [95% CI]: 9.50 [5.23-13.76], t (11) = 4.90, P = 0.0005) and 3 months (mean difference [95% CI]: 9.99 [4.71-15.27], t (6) = 4.63, P = 0.0036) postinfusion. Widespread changes in FC followed infusion, which correlated with EPDS scores. IMPLICATIONS/CONCLUSIONS: Brexanolone is a successful treatment for PPD in the clinical setting. In conjunction with routine clinical care, brexanolone was linked to a reduction in symptoms lasting at least 3 months. pfMRI is feasible in postpartum patients receiving brexanolone and has the potential to elucidate individual-specific mechanisms of action.

Functional neuroimaging in patients with catatonia: A systematic review.

BACKGROUND: Catatonia is a challenging and heterogeneous neuropsychiatric syndrome of motor, affective and behavioral dysregulation which has been associated with multiple disorders such as structural brain lesions, systemic diseases, and psychiatric disorders. This systematic review summarized and compared functional neuroimaging abnormalities in catatonia associated with psychiatric and medical conditions. METHODS: Using PRISMA methods, we completed a systematic review of 6 databases from inception to February 7th, 2024 of patients with catatonia that had functional neuroimaging performed. RESULTS: A total of 309 studies were identified through the systematic search and 62 met the criteria for full-text review. A total of 15 studies reported patients with catatonia associated with a psychiatric disorder (n = 241) and one study reported catatonia associated with another medical condition, involving patients with N-methyl-d-aspartate receptor antibody encephalitis (n = 23). Findings varied across disorders, with hyperactivity observed in areas like the prefrontal cortex (PFC), the supplementary motor area (SMA) and the ventral pre-motor cortex in acute catatonia associated to a psychiatric disorder, hypoactivity in PFC, the parietal cortex, and the SMA in catatonia associated to a medical condition, and mixed metabolic activity in the study on catatonia linked to a medical condition. CONCLUSION: Findings support the theory of dysfunction in cortico-striatal-thalamic, cortico-cerebellar, anterior cingulate-medial orbitofrontal, and lateral orbitofrontal networks in catatonia. However, the majority of the literature focuses on schizophrenia spectrum disorders, leaving the pathophysiologic characteristics of catatonia in other disorders less understood. This review highlights the need for further research to elucidate the pathophysiology of catatonia across various disorders.

The neuroanatomy of developmental language disorder: a systematic review and meta-analysis.

Developmental language disorder (DLD) is a common neurodevelopmental disorder with adverse impacts that continue into adulthood. However, its neural bases remain unclear. Here we address this gap by systematically identifying and quantitatively synthesizing neuroanatomical studies of DLD using co-localization likelihood estimation, a recently developed neuroanatomical meta-analytic technique. Analyses of structural brain data (22 peer-reviewed papers, 577 participants) revealed highly consistent anomalies only in the basal ganglia (100% of participant groups in which this structure was examined, weighted by group sample sizes; 99.8% permutation-based likelihood the anomaly clustering was not due to chance). These anomalies were localized specifically to the anterior neostriatum (again 100% weighted proportion and 99.8% likelihood). As expected given the task dependence of activation, functional neuroimaging data (11 peer-reviewed papers, 414 participants) yielded less consistency, though anomalies again occurred primarily in the basal ganglia (79.0% and 95.1%). Multiple sensitivity analyses indicated that the patterns were robust. The meta-analyses elucidate the neuroanatomical signature of DLD, and implicate the basal ganglia in particular. The findings support the procedural circuit deficit hypothesis of DLD, have basic research and translational implications for the disorder, and advance our understanding of the neuroanatomy of language.

Optimized microburst VNS elicits fMRI responses beyond thalamic-specific response from standard VNS.

OBJECTIVE: In parallel to standard vagus nerve stimulation (VNS), microburst stimulation delivery has been developed. We evaluated the fMRI-related signal changes associated with standard and optimized microburst stimulation in a proof-of-concept study (NCT03446664). METHODS: Twenty-nine drug-resistant epilepsy patients were prospectively implanted with VNS. Three 3T fMRI scans were collected 2 weeks postimplantation. The maximum tolerated VNS intensity was determined prior to each scan starting at 0.125 mA with 0.125 mA increments. FMRI scans were block-design with alternating 30 sec stimulation [ON] and 30 sec no stimulation [OFF]: Scan 1 utilized standard VNS and Scan 3 optimized microburst parameters to determine target settings. Semi-automated on-site fMRI data processing utilized ON-OFF block modeling to determine VNS-related fMRI activation per stimulation setting. Anatomical thalamic mask was used to derive highest mean thalamic t-value for determination of microburst stimulation parameters. Paired t-tests corrected at P < 0.05 examined differences in fMRI responses to each stimulation type. RESULTS: Standard and microburst stimulation intensities at Scans 1 and 3 were similar (P = 0.16). Thalamic fMRI responses were obtained in 28 participants (19 with focal; 9 with generalized seizures). Group activation maps showed standard VNS elicited thalamic activation while optimized microburst VNS showed widespread activation patterns including thalamus. Comparison of stimulation types revealed significantly greater cerebellar, midbrain, and parietal fMRI signal changes in microburst compared to standard VNS. These differences were not associated with seizure responses. INTERPRETATION: While standard and optimized microburst VNS elicited thalamic activation, microburst also engaged other brain regions. Relationship between these fMRI activation patterns and clinical response warrants further investigation. CLINICAL TRIAL REGISTRATION: The study was registered with clinicaltrials.gov (NCT03446664).

Brain Circuit-Derived Biotypes for Treatment Selection in Mood Disorders: A Critical Review and Illustration of a Functional Neuroimaging Tool for Clinical Translation.

Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging-derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice.

Fractionating difficulty during sentence comprehension using functional neuroimaging.

Sentence comprehension is highly practiced and largely automatic, but this belies the complexity of the underlying processes. We used functional neuroimaging to investigate garden-path sentences that cause difficulty during comprehension, in order to unpack the different processes used to support sentence interpretation. By investigating garden-path and other types of sentences within the same individuals, we functionally profiled different regions within the temporal and frontal cortices in the left hemisphere. The results revealed that different aspects of comprehension difficulty are handled by left posterior temporal, left anterior temporal, ventral left frontal, and dorsal left frontal cortices. The functional profiles of these regions likely lie along a spectrum of specificity to generality, including language-specific processing of linguistic representations, more general conflict resolution processes operating over linguistic representations, and processes for handling difficulty in general. These findings suggest that difficulty is not unitary and that there is a role for a variety of linguistic and non-linguistic processes in supporting comprehension.