Functional connectome biotypes of chemotherapy-related cognitive impairment

Comparative effectiveness of interventions for cancer treatment-related cognitive impairment in adult cancer survivors: protocol for a systematic review

BACKGROUND

Cancer treatment-related cognitive impairment (CTRCI) can substantially reduce the quality of life of cancer survivors. Many treatments of CTRCI have been evaluated in randomized controlled trials (RCTs), including psychological interventions, pharmacologic interventions, and other therapies. There is a pressing need to establish the benefits and harms of previously studied CTRCI treatments. The proposed systematic review and network meta-analyses will assess the relative efficacy and safety of competing interventions for the management of CTRCI.

METHODS

In consultation with the review team, an experienced medical information specialist will draft electronic search strategies for MEDLINE®, Embase, CINAHL, PsycINFO, and the Cochrane Trials Registry. We will seek RCTs of interventions for the treatment of CTRCI in adults with any cancer, except cancers/metastases of the central nervous system. Due to the anticipated high search yields, dual independent screening of citations will be expedited by use of an artificial intelligence/machine learning tool. The co-primary outcomes of interest will be subjective and objective cognitive function. Secondary outcomes of interest will include measures of quality of life, mental and physical health symptoms, adherence to treatment, and harms (overall and treatment-related harms and harms associated with study withdrawal), where feasible, random-effects meta-analyses and network meta-analyses will be pursued. We will address the anticipated high clinical and methodological heterogeneity through meta-regressions, subgroup analyses, and/or sensitivity analyses.

DISCUSSION

The proposed systematic review will deliver a robust comparative evaluation of the efficacy and safety of existing therapies for the management of CTRCI. These findings will inform clinical decisions, identify evidence gaps, and identify promising therapies for future evaluation in RCTs.

Evidence of compensatory neural hyperactivity in a subgroup of chemotherapy-treated breast cancer survivors and its association with brain aging

Chemotherapy-related cognitive impairment (CRCI) remains poorly understood in terms of the mechanisms of cognitive decline. Neural hyperactivity has been reported on average in cancer survivors, but it is unclear which patients demonstrate this neurophenotype, limiting precision medicine in this population. We evaluated a retrospective sample of 80 breast cancer survivors and 80 non-cancer controls, age 35-73, for which we had previously identified and validated three data-driven, biological subgroups (biotypes) of CRCI. We measured neural activity using the z-normalized percent amplitude of fluctuation from resting state functional magnetic resonance imaging (MRI). We tested established, quantitative criteria to determine if hyperactivity can accurately be considered compensatory. We also calculated brain age gap by applying a previously validated algorithm to anatomic MRI. We found that neural activity differed across the three CRCI biotypes and controls (F = 13.5, p < 0.001), with Biotype 2 demonstrating significant hyperactivity compared to the other groups (p < 0.004, corrected), primarily in prefrontal regions. Alternatively, Biotypes 1 and 3 demonstrated significant hypoactivity (p < 0.02, corrected). Hyperactivity in Biotype 2 met several of the criteria to be considered compensatory. However, we also found a positive relationship between neural activity and brain age gap in these patients (r = 0.45, p = 0.042). Our results indicated that neural hyperactivity is specific to a subgroup of breast cancer survivors and, while it seems to support preserved cognitive function, it could also increase the risk of accelerated brain aging. These findings could inform future neuromodulatory interventions with respect to the risks and benefits of up or downregulation of neural activity.

Cognitive alterations and brain functional changes following chemotherapy treatment in breast cancer patients: A systematic review on resting-state fMRI studies

Cognitive dysfunctions and functional brain modifications are among the side effects reported by breast cancer patients that persist beyond the chemotherapy. This paper aims at synthesizing the evidence on cognitive and functional brain changes and their associations in breast cancer patients treated with chemotherapy. A systematic literature search was performed using PubMed, Ovid MEDLINE, Scopus, and Embase up to July 2022. Eligible studies evaluated adult women with breast cancer treated with systemic chemotherapy, that performed cognitive assessment and resting-state functional MRI. Methodological quality was assessed. Sixteen studies were included, with a total of 1054 female participants. All studies reported alterations mainly concerned the fronto-parieto-temporal system and specifically involved the disruption of the DMN. Consistent with these findings, BCPs showed changes in cognitive performance reporting dysfunctions in executive ability, memory, and attention. However, not all the studies found a significant association between functional brain alterations and cognitive dysfunction. Some limitations including lack of sample homogeneity and different methodological approaches were reported. This work highlighted the presence of cognitive dysfunctions and functional brain alteration in breast cancer patients treated with chemotherapy. This allows a greater awareness of the side effects, promoting better clinical management. However, further research is needed to investigate the cause-effect relationship between cognitive and functional alterations.

Cancer-related cognitive impairment in racial and ethnic minority groups: a scoping review

PURPOSE

Disparities in cognitive function among racial and ethnic groups have been reported in non-cancer conditions, but cancer-related cognitive impairment (CRCI) in racial and ethnic minority groups is poorly understood. We aimed to synthesize and characterize the available literature about CRCI in racial and ethnic minority populations.

METHODS

We conducted a scoping review in the PubMed, PsycInfo, and Cumulative Index to Nursing and Allied Health Literature databases. Articles were included if they were published in English or Spanish, reported cognitive functioning in adults diagnosed with cancer, and characterized the race or ethnicity of the participants. Literature reviews, commentaries, letters to the editor, and gray literature were excluded.

RESULTS

Seventy-four articles met the inclusion criteria, but only 33.8% differentiated the CRCI findings by racial or ethnic subgroups. There were associations between cognitive outcomes and the participants' race or ethnicity. Additionally, some studies found that Black and non-white individuals with cancer were more likely to experience CRCI than their white counterparts. Biological, sociocultural, and instrumentation factors were associated with CRCI differences between racial and ethnic groups.

CONCLUSIONS

Our findings indicate that racial and ethnic minoritized individuals  may be disparately affected by CRCI. Future research should use standardized guidelines for measuring and reporting the self-identified racial and ethnic composition of the sample; differentiate CRCI findings by racial and ethnic subgroups; consider the influence of structural racism in health outcomes; and develop strategies to promote the participation of members of racial and ethnic minority groups.

Neuroimaging based biotypes for precision diagnosis and prognosis in cancer-related cognitive impairment

Cancer related cognitive impairment (CRCI) is commonly associated with cancer and its treatments, yet the present binary diagnostic approach fails to capture the full spectrum of this syndrome. Cognitive function is highly complex and exists on a continuum that is poorly characterized by dichotomous categories. Advanced statistical methodologies applied to symptom assessments have demonstrated that there are multiple subclasses of CRCI. However, studies suggest that relying on symptom assessments alone may fail to account for significant differences in the neural mechanisms that underlie a specific cognitive phenotype. Treatment plans that address the specific physiologic mechanisms involved in an individual patient's condition is the heart of precision medicine. In this narrative review, we discuss how biotyping, a precision medicine framework being utilized in other mental disorders, could be applied to CRCI. Specifically, we discuss how neuroimaging can be used to determine biotypes of CRCI, which allow for increased precision in prediction and diagnosis of CRCI via biologic mechanistic data. Biotypes may also provide more precise clinical endpoints for intervention trials. Biotyping could be made more feasible with proxy imaging technologies or liquid biomarkers. Large cross-sectional phenotyping studies are needed in addition to evaluation of longitudinal trajectories, and data sharing/pooling is highly feasible with currently available digital infrastructures.

Behavioral and biologic characteristics of cancer-related cognitive impairment biotypes

Psychiatric diagnosis is moving away from symptom-based classification and towards multi-dimensional, biologically-based characterization, or biotyping. We previously identified three biotypes of chemotherapy-related cognitive impairment based on functional brain connectivity. In this follow-up study of 80 chemotherapy-treated breast cancer survivors and 80 non-cancer controls, we evaluated additional factors to help explain biotype expression: neurofunctional stability, brain age, apolipoprotein (APOE) genotype, and psychoneurologic symptoms. We also compared the discriminative ability of a traditional, symptom-based cognitive impairment definition with that of biotypes. We found significant differences in cortical brain age (F = 10.50, p < 0.001), neurofunctional stability (F = 2.83, p = 0.041), APOE e4 genotype (X2 = 7.68, p = 0.050), and psychoneurological symptoms (Pillai = 0.378, p < 0.001) across the three biotypes. The more resilient Biotype 2 demonstrated significantly higher neurofunctional stability compared to the other biotypes. Symptom-based classification of cognitive impairment did not differentiate biologic or other behavioral variables, suggesting that traditional categorization of cancer-related cognitive effects may miss important characteristics which could inform targeted treatment strategies. Additionally, biotyping, but not symptom-typing, was able to distinguish survivors with cognitive versus psychological effects. Our results suggest that Biotype 1 survivors might benefit from first addressing symptoms of anxiety and fatigue, Biotype 3 might benefit from a treatment plan which includes sleep hygiene, and Biotype 2 might benefit most from cognitive skills training or rehabilitation. Future research should include additional demographic and clinical information to further investigate biotype expression related to risk and resilience and examine integration of more clinically feasible imaging approaches.

Remote assessment of cognitive dysfunction in hematologic malignancies using web-based neuropsychological testing

BACKGROUND

Cognitive impairment is a frequent adverse effect of cancer and its therapies. As neuropsychological assessment is not often standard of care for patients with non-CNS disease, efficient, practical assessment tools are required to track cognition across the disease course. We examined cognitive functioning using a web-based cognitive testing battery to determine if it could detect differences between patients with cancer and controls.

METHODS

We enrolled 22 patients with multiple myeloma (MM) or non-Hodgkin lymphoma (NHL) and 40 healthy controls (mean age = 56 ± 11 years, 52% male). Participants completed the BrainCheck cognitive testing battery and online versions of select measures from the Patient Reported Outcome Measures Information System (PROMIS) during a video conference. MANOVA was used to compare BrainCheck and PROMIS scores between groups controlling for age and sex. An exploratory linear regression analysis was conducted within the cancer group to determine potential contributors to cognitive functioning.

RESULTS

All participants except for one control completed the online assessment measures without difficulty. Compared to controls, the cancer group demonstrated significantly lower scores in objective and subjective cognitive function, physical functioning, and social role performance and elevated fatigue scores. Corticosteroid treatment, immunotherapy, lower physical functioning, lower income, and older age significantly contributed to lower cognitive function (adjusted R²  = 0.925, F = 19.63, p = 0.002).

CONCLUSION

Remote assessment of cognitive and psychosocial functioning is feasible with patients with cancer following treatments. The BrainCheck cognitive testing battery has the potential to detect differences in cognition between patients with cancer and controls.

Diagnostic Accuracy of Machine-Learning Models on Predicting Chemo-Brain in Breast Cancer Survivors Previously Treated with Chemotherapy: A Meta-Analysis

We performed a meta-analysis of chemo-brain diagnostic, pooling sensitivities, and specificities in order to assess the accuracy of a machine-learning (ML) algorithm in breast cancer survivors previously treated with chemotherapy. We searched PubMed, Web of Science, and Scopus for eligible articles before 30 September 2022. We identified three eligible studies from which we extracted seven ML algorithms. For our data, the χ² tests demonstrated the homogeneity of the sensitivity's models (χ² = 7.6987, df = 6, p-value = 0.261) and the specificities of the ML models (χ² = 3.0151, df = 6, p-value = 0.807). The pooled area under the curve (AUC) for the overall ML models in this study was 0.914 (95%CI: 0.891-0.939) and partial AUC (restricted to observed false positive rates and normalized) was 0.844 (95%CI: 0.80-0.889). Additionally, the pooled sensitivity and pooled specificity values were 0.81 (95% CI: 0.75-0.86) and 0.82 (95% CI: 0.76-0.86), respectively. From all included ML models, support vector machine demonstrated the best test performance. ML models represent a promising, reliable modality for chemo-brain prediction in breast cancer survivors previously treated with chemotherapy, demonstrating high accuracy.

Biotypes of HIV-associated neurocognitive disorders based on viral and immune pathogenesis

PURPOSE OF REVIEW

HIV-associated neurocognitive disorders (HAND) continues to be prevalent in people living with HIV despite antiretroviral therapy. However, understanding disease mechanisms and identifying therapeutic avenues has been challenging. One of the challenges is that HAND is a heterogeneous disease and that patients identified with similar impairments phenotypically may have very different underlying disease processes. As the NeuroAIDS field is re-evaluating the approaches used to identify patients with HIV-associated neurological impairments, we propose the subtyping of patients into biotypes based on viral and immune pathogenesis.

RECENT FINDINGS

Here we review the evidence supporting subtyping patients with HIV-associated neurological complications into four biotypes: macrophage-mediated HIV encephalitis, CNS viral escape, T-cell-mediated HIV encephalitis, and HIV protein-associated encephalopathy.

SUMMARY

Subtyping patients into subgroups based on biotypes has emerged as a useful approach for studying heterogeneous diseases. Understanding biotypes of HIV-associated neurocognitive impairments may therefore enable better understanding of disease mechanisms, allow for the development of prognostic and diagnostic markers, and could ultimately guide therapeutic decisions.

Classification of Chemotherapy-Related Subjective Cognitive Complaints in Breast Cancer Using Brain Functional Connectivity and Activity: A Machine Learning Analysis

The aim of this study was combining multi-level resting-state functional magnetic resonance imaging (rs-fMRI) features with machine learning method to distinguish breast cancer patients with chemotherapy-related subjective cognitive complaints (SCC) from non-chemotherapy (BC) and healthy controls (HC). Forty subjects in SCC group, forty-nine in BC group and thirty-four in HC group were recruited and underwent rs-fMRI scanning. Based on the anatomical automatic labeling brain atlas, the functional metrics of all subjects included functional connectivity, amplitude of low frequency fluctuation and fractional amplitude of low frequency fluctuation, regional homogeneity, voxel-mirrored homotopic connectivity and degree centrality were calculated and extracted as features set. Then, the rs-fMRI features were selected by two-sample t-test, removing variables with a high pairwise correlation and least absolute shrinkage and selection operator regression. Finally, the support vector machine models were built for classification (SCC vs. BC, SCC vs. HC). Thirty-eight features (SCC vs. BC) and seventeen features (SCC vs. HC) were selected separately, and the accuracy of the models were 82.0% and 91.9%, respectively. These findings demonstrated a valid machine learning approach that effectively distinguished breast cancer patients with chemotherapy-related SCC from non-chemotherapy and healthy controls, providing potential neuroimaging evidence for early diagnosis and clinical intervention of chemotherapy-related SCC.

Cross-Sectional Characterization of Local Brain Network Connectivity Pre and Post Breast Cancer Treatment and Distinct Association With Subjective Cognitive and Psychological Function

Objective: We aimed to characterize local brain network connectivity in long-term breast cancer survivors compared to newly diagnosed patients. Methods: Functional magnetic resonance imaging (fMRI) and subjective cognitive and psychological function data were obtained from a group of 76 newly diagnosed, pre-treatment female patients with breast cancer (mean age 57 ± 7 years) and a separate group of 80, post-treatment, female breast cancer survivors (mean age 58 ± 8; mean time since treatment 44 ± 43 months). The network-based statistic (NBS) was used to compare connectivity of local brain edges between groups. Hubs were defined as nodes with connectivity indices one standard deviation or more above network mean and were further classified as provincial (higher intra-subnetwork connectivity) or connector (higher inter-subnetwork connectivity) using the participation coefficient. We determined the hub status of nodes encompassing significantly different edges and correlated the centralities of edges with behavioral measures. Results: The post-treatment group demonstrated significantly lower subjective cognitive function (W = 3,856, p = 0.004) but there were no group differences in psychological distress (W = 2,866, p = 0.627). NBS indicated significantly altered connectivity (p < 0.042, corrected) in the post-treatment group compared to the pre-treatment group largely in temporal, frontal-temporal and temporal-parietal areas. The majority of the regions projecting these connections (78%) met criteria for hub status and significantly less of these hubs were connectors in the post-treatment group (z = 1.85, p = 0.031). Subjective cognitive function and psychological distress were correlated with largely non-overlapping edges in the post-treatment group (p < 0.05). Conclusion: Widespread functional network alterations are evident in long-term survivors of breast cancer compared to newly diagnosed patients. We also demonstrated that there are both overlapping and unique brain network signatures for subjective cognitive function vs. psychological distress.

Cognitive and Imaging Differences After Proton and Photon Whole Brain Irradiation in a Preclinical Model

Purpose:

Compared with photon cranial radiation therapy (X-CRT), proton cranial radiation therapy (P-CRT) offers potential advantages in limiting radiation-induced sequalae in the treatment of pediatric brain tumors. This study aims to identify cognitive, functional magnetic resonance and positron emission tomography imaging markers and molecular differences between the radiation modalities.

Methods and Materials:

Juvenile rats received a single faction of 10 Gy (relative biological effectiveness−weighted dose) delivered with 6 MV X-CRT or at the midspread out Bragg peak of a 100 MeV P-CRT beam. At 3, 6, and 12 months post-CRT, executive function was measured using 5-choice serial reaction time task. At ~12 months post-CRT, animals were imaged with ¹⁸F-Flurodeoxy-glucose positron emission tomography imaging followed by functional ex vivo magnetic resonance imaging and stained for markers of neuroinflammation.

Results:

Irradiated animals had cognitive impairment with a higher number of omissions and lower incorrect and premature responses compared with sham (P ≤ .05). The accuracy of the animals’ X-CRT was less than that of sham (P ≤ .001). No significant difference in rates of cognitive change were found between the radiation modalities. At 12 months post-CRT, glucose metabolism was significantly higher than sham in X-CRT (P = .04) but not P-CRT. Using diffusion tensor imaging, P-CRT brains were found to have higher white matter volume and fiber lengths compared with sham (P < .03). Only X-CRT animals had higher apparent diffusion coefficient values compared with sham (P = .04). P-CRT animals had more connectomic changes compared with X-CRT. Correlative analysis identified several imaging features with cognitive performance. Further-more, microgliosis (P < .05), astrogliosis (P < .01), and myelin thinning (P <.05) were observed in both radiation modalities, with X-CRT showing slightly more inflammation.

Conclusions:

Both P-CRT and X-CRT lead to neurocognitive changes compared with sham. Although no significant difference was observed in neuroinflammation between the irradiated groups, differences were found in late-term glucose metabolism and brain connectome. Our results indicate that despite relative biological effectiveness weighting of the proton dose there are still differential effects which warrants further investigation.

Changes in Brain Functional and Effective Connectivity After Treatment for Breast Cancer and Implications for Intervention Targets

Background: Patients with breast cancer frequently report cognitive impairment both during and after completion of therapy. Evidence suggests that cancer-related cognitive impairments are related to widespread neural network dysfunction. The default mode network (DMN) is a large conserved network that plays a critical role in integrating the functions of various neural systems. Disruption of the network may play a key role in the development of cognitive impairment. Methods: We compared neuroimaging and neurocognitive data from 43 newly diagnosed primary breast cancer patients (mean age = 48, standard deviation [SD] = 8.9 years) and 50 frequency-matched healthy female controls (mean age = 50, SD = 10 years) before treatment and 1 year after treatment completion. Functional and effective connectivity measures of the DMN were obtained using graph theory and Bayesian network analysis methods, respectively. Results: Compared with healthy females, the breast cancer group displayed higher global efficiency and path length post-treatment (p < 0.03, corrected). Breast cancer survivors showed significantly lower performance on measures of verbal memory, attention, and verbal fluency (p < 0.05) at both time points. Within the DMN, local brain network organization, as measured by edge-betweenness centralities, was significantly altered in the breast cancer group compared with controls at both time points (p < 0.0001, corrected), with several connections showing a significant group-by-time effect (p < 0.003, corrected). Effective connectivity demonstrated significantly altered patterns of neuronal coupling in patients with breast cancer (p < 0.05). Significant correlations were seen between hormone blockade therapy, radiation therapy, chemotherapy cycles, memory, and verbal fluency test and edge-betweenness centralities. Discussion: This pattern of altered network organization in the default mode is believed to result in reduced network efficiency and disrupted communication. Subregions of the DMN, the orbital prefrontal cortex and posterior memory network, appear to be at the center of this disruption and this could inform future interventions.