Skip to content

Electrophysiological Biomarkers of Chemotherapy-related Cognitive Impairment and Recovery

Electrophysiological Biomarkers of Chemotherapy-related Cognitive Impairment and Recovery

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT02767388
Acronym
Chemobrain
Enrollment
45
Registered
2016-05-10
Start date
2016-09-01
Completion date
2018-03-15
Last updated
2023-09-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Acute Lymphoid Leukemia, Acute Myeloid Leukemia, Chronic Lymphocytic Leukemia, Chronic Myeloid Leukemia, Effects of Chemotherapy, Mild Cognitive Impairment, Multiple Myeloma, Myelodysplastic Syndrome, Non-hodgkin Lymphoma

Brief summary

Broadly speaking, the goal of this study is to better understand the influence of chemotherapy treatment on the cognitive and neural mechanisms underlying human behavior. Extant literature lacks diversity in studied cancer populations and treatment protocols, and provides limited understanding of the cognitive abilities that are impaired by chemotherapy. To overcome these limitations, this study will employ a sophisticated battery of tests on an understudied cancer population. Eligible participants will either be patients diagnosed with hematological malignancy (HM) or demographically matched healthy control patients. After HM diagnosis and treatment protocols have been established, patients will be inducted into the longitudinal study comprised of three visits: 1) after diagnosis but prior to chemotherapy treatment (baseline), 2) after one treatment cycle (one month post-baseline), and 3) after three treatment cycles (three months post-baseline). Patients will undergo a test battery designed to measure specific behavioral and neural mechanisms of attention; tests will either be computer-based cognitive tasks or simulated driving tests that immerse patients into virtual driving scenarios. During each test, EEG will be concurrently measured through non-invasive scalp electrophysiology recordings; EEG recordings will reveal underlying neural mechanisms affected by chemotherapy. Additionally, neuropsychological tests of vision, attention, and memory will be administered, as well as questionnaires to evaluate health, mobility, and life space. Finally, blood samples will be collected to examine levels of circulating inflammation-specific proteins typically present in cancer patients. This study will allow us to better understand the mechanisms through which chemotherapy influences cognitive performance. Results from this study will influence the administration of chemotherapy treatments so that patients can continue to receive the highest medical care while maintaining optimal cognitive abilities and quality of life.

Detailed description

The broad goal of this research project is to develop a core set of biomarkers for chemotherapy-related cognitive impairment (or chemobrain). Clinical studies have documented mild cognitive impairment in chemotherapy patients most frequently within the domains of attention and memory, though impairments have been observed across a broad range of cognitive abilities. In addition, neuroimaging studies have demonstrated chemotherapy-related structural and functional changes in distributed cortical areas, including regions of the fronto-parietal attention network. While these studies suggest chemotherapy treatment negatively impacts patient health and cognitive function, it remains unclear how chemotherapy affects neural mechanisms of cognitive abilities. Current literature is limited in four major ways: (1) most research has focused on breast cancer populations, providing little insight into impact of tumor type, (2) few studies have examined the parametric effects of chemotherapy toxicity, (3) neuropsychological exams provide weak resolution of specific cognitive functions, and (4) neural factors associated with cognitive impairment are difficult to dissociate from non-neural (e.g. psychosocial) factors. To overcome these central limitations, the investigators propose a one-year longitudinal study that aims to systematically examine the influence of cancer stage and treatment toxicity on mild cognitive impairment observed in hematological malignancy (HM) patients by implementing a core battery of behavioral and neural measures of attention. Our specific aims (SA) are to: SA1: Quantify chemotherapy-related impairments of attention-specific processes in HM patients. H1a: No difference in behavioral measures of attention will be observed across HM groups prior to treatment, and HM groups will perform worse than healthy controls. H1b: Exposure to chemotherapy will predict behavioral impairments of attention, and the magnitude of impairment will be linked with treatment toxicity. SA2: Quantify electrophysiological measures of attention-specific processes and determine the link between chemotherapy-related impairments in neural activity and cognitive ability. H2a: No difference in electrophysiological measures of attention will be observed across HM and healthy control groups prior to treatment. H2b: Exposure to chemotherapy will predict functional impairments in electrophysiological measures of attention, and the magnitude of impairment will be linked with treatment toxicity. H2c: Chemotherapy-related impairment in neural measures of attention will be predicted by concurrent impairments in behavioral measures of attention (as in H1b). SA3: Implement controlled simulations of on-road driving scenarios that probe specific attention processes to determine the impact of chemotherapy on complex real-world behavior. H3a: No difference in driving performance will be observed across HM groups prior to treatment, and driving performance will be better in healthy controls compared to HM patients. H3b: Exposure to chemotherapy will predict greater impairment in simulated driving performance, and the magnitude of impairment will be linked with treatment toxicity. H3c: Impairments in behavioral (as in H1a) and neural measures (as in H2a) of attention will predict greater impairment in simulated on-road driving performance. Our empirical approach will allow us to more rigorously study the neural mechanisms of chemotherapy-related cognitive impairment. The current proposal aims to extend previous research by longitudinally investigating an understudied cancer population whose constituents are assigned to a treatment group at diagnosis, thus providing sufficient experimental control for examining parametric effects of cancer burden and treatment toxicity on specific mechanisms of attention. Results obtained from this study will be critical to understanding risk factors associated with chemotherapy, which will allow clinicians to make informed treatment recommendations in order to reduce the likelihood of cognitive impairment and maintain the highest quality of life possible for the ever-increasing cancer survivor population.

Interventions

None listed

Sponsors

University of Nebraska
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
19 Years to 80 Years
Healthy volunteers
Yes

Inclusion criteria

* HM diagnosis * scheduled to receive treatment based on risk classification * between 19 to 80 years of age- * normal or corrected-to-normal vision * matched to HM patient demographics (healthy controls)

Exclusion criteria

* non-HM non-cutaneous cancer diagnosis (patients with localized skin cancer may not be excluded) * prior radiation or chemotherapy treatment * HM cancer diagnosis (healthy controls)

Design outcomes

Primary

MeasureTime frameDescription
Change From Baseline Capture Task Performance at 1- and 3- MonthsCollected at Study Induction, 1 month after Study Induction, 3 months after Study InductionResponse time - measured as the time required to respond to a target hidden among distractor items - is the primary outcome measure of the capture task. Proportional response time was calculated by subtracting mean response time in the neutral condition from response time in the capture condition, and dividing that number by the standard deviation of response time across conditions. Changes in proportional response time across study visits is reported. Positive values correspond to an increase in response time and negative values correspond to a decrease in response time.
Change From Baseline N2pc Amplitude at 1- and 3- MonthsCollected at Study Induction, 1 month after Study Induction, 3 months after Study InductionElectrophysiological component that measures allocation of attentional resources
Change From Baseline Filter Task Performance at 1- and 3- MonthsCollected at Study Induction, 1 month after Study Induction, 3 months after Study InductionResponse accuracy - measured as the proportion of correct trials - is the primary outcome measure of the filter task. Changes in response accuracy were calculated by subtracting response accuracy at 1-month and 3-months from baseline response accuracy. Positive values correspond to an increase in accuracy and negative values correspond to a decline in accuracy.
Change From Baseline CDA Amplitude at 1- and 3- MonthsCollected at Study Induction, 1 month after Study Induction, 3 months after Study InductionElectrophysiological component that measures online storage load

Countries

United States

Participant flow

Participants by arm

ArmCount
HM - Chemotherapy
Study patients diagnosed with HM that are scheduled to receive chemotherapy treatment.
15
HM - No Chemotherapy
Study patients diagnosed with HM that are scheduled to receive non-chemotherapy treatment options.
15
Healthy Controls
Study participants that are demographically matched to HM study patients and meet all inclusion criteria
15
Total45

Baseline characteristics

CharacteristicHealthy ControlsTotalHM - ChemotherapyHM - No Chemotherapy
Age, Continuous60.0 years
STANDARD_DEVIATION 16.5
60.6 years
STANDARD_DEVIATION 14.5
59.3 years
STANDARD_DEVIATION 15.2
63.2 years
STANDARD_DEVIATION 10.9
Race (NIH/OMB)
American Indian or Alaska Native
0 Participants0 Participants0 Participants0 Participants
Race (NIH/OMB)
Asian
0 Participants0 Participants0 Participants0 Participants
Race (NIH/OMB)
Black or African American
0 Participants1 Participants0 Participants1 Participants
Race (NIH/OMB)
More than one race
0 Participants0 Participants0 Participants0 Participants
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
0 Participants0 Participants0 Participants0 Participants
Race (NIH/OMB)
Unknown or Not Reported
0 Participants0 Participants0 Participants0 Participants
Race (NIH/OMB)
White
15 Participants44 Participants15 Participants14 Participants
Region of Enrollment
United States
15 participants45 participants15 participants15 participants
Sex: Female, Male
Female
8 Participants24 Participants7 Participants9 Participants
Sex: Female, Male
Male
7 Participants21 Participants8 Participants6 Participants

Adverse events

Event typeEG000
affected / at risk
EG001
affected / at risk
EG002
affected / at risk
deaths
Total, all-cause mortality
0 / 150 / 150 / 15
other
Total, other adverse events
0 / 150 / 150 / 15
serious
Total, serious adverse events
0 / 150 / 150 / 15

Outcome results

Primary

Change From Baseline Capture Task Performance at 1- and 3- Months

Response time - measured as the time required to respond to a target hidden among distractor items - is the primary outcome measure of the capture task. Proportional response time was calculated by subtracting mean response time in the neutral condition from response time in the capture condition, and dividing that number by the standard deviation of response time across conditions. Changes in proportional response time across study visits is reported. Positive values correspond to an increase in response time and negative values correspond to a decrease in response time.

Time frame: Collected at Study Induction, 1 month after Study Induction, 3 months after Study Induction

Population: The number analyzed differed between rows due to participant dropout from Month-1 to Month-3. Differences in number analyzed and numbers in the Participant Flow module were due to patient dropout and either: (1) a minority of patients requesting not to complete testing; or (2) difficulties with electrophysiology or stimulus presentation equipment.

ArmMeasureGroupValue (MEAN)Dispersion
HM - ChemotherapyChange From Baseline Capture Task Performance at 1- and 3- MonthsChange at 1-Month.103 change in proportional response timeStandard Deviation 0.223
HM - ChemotherapyChange From Baseline Capture Task Performance at 1- and 3- MonthsChange at 3-Month.067 change in proportional response timeStandard Deviation 0.113
HM - No ChemotherapyChange From Baseline Capture Task Performance at 1- and 3- MonthsChange at 1-Month-.039 change in proportional response timeStandard Deviation 0.19
HM - No ChemotherapyChange From Baseline Capture Task Performance at 1- and 3- MonthsChange at 3-Month-.002 change in proportional response timeStandard Deviation 0.191
Healthy ControlsChange From Baseline Capture Task Performance at 1- and 3- MonthsChange at 1-Month.066 change in proportional response timeStandard Deviation 0.201
Healthy ControlsChange From Baseline Capture Task Performance at 1- and 3- MonthsChange at 3-Month-.084 change in proportional response timeStandard Deviation 0.165
Primary

Change From Baseline CDA Amplitude at 1- and 3- Months

Electrophysiological component that measures online storage load

Time frame: Collected at Study Induction, 1 month after Study Induction, 3 months after Study Induction

Population: The number analyzed differed between rows due to participant dropout from Month-1 to Month-3. Differences in number analyzed and numbers in the Participant Flow module were due to patient dropout and either: (1) a minority of patients requesting not to complete testing; or (2) difficulties with electrophysiology or stimulus presentation equipment.

ArmMeasureGroupValue (MEAN)Dispersion
HM - ChemotherapyChange From Baseline CDA Amplitude at 1- and 3- MonthsChange at 1-Month.30 microvoltsStandard Deviation 0.29
HM - ChemotherapyChange From Baseline CDA Amplitude at 1- and 3- MonthsChange at 3-Month-.34 microvoltsStandard Deviation 0.43
HM - No ChemotherapyChange From Baseline CDA Amplitude at 1- and 3- MonthsChange at 1-Month-.22 microvoltsStandard Deviation 0.91
HM - No ChemotherapyChange From Baseline CDA Amplitude at 1- and 3- MonthsChange at 3-Month-.10 microvoltsStandard Deviation 0.87
Healthy ControlsChange From Baseline CDA Amplitude at 1- and 3- MonthsChange at 1-Month-.09 microvoltsStandard Deviation 0.55
Healthy ControlsChange From Baseline CDA Amplitude at 1- and 3- MonthsChange at 3-Month-.27 microvoltsStandard Deviation 0.77
Primary

Change From Baseline Filter Task Performance at 1- and 3- Months

Response accuracy - measured as the proportion of correct trials - is the primary outcome measure of the filter task. Changes in response accuracy were calculated by subtracting response accuracy at 1-month and 3-months from baseline response accuracy. Positive values correspond to an increase in accuracy and negative values correspond to a decline in accuracy.

Time frame: Collected at Study Induction, 1 month after Study Induction, 3 months after Study Induction

Population: The number analyzed differed between rows due to participant dropout from Month-1 to Month-3. Differences in number analyzed and numbers in the Participant Flow module were due to patient dropout and either: (1) a minority of patients requesting not to complete testing; or (2) difficulties with electrophysiology or stimulus presentation equipment.

ArmMeasureGroupValue (MEAN)Dispersion
HM - ChemotherapyChange From Baseline Filter Task Performance at 1- and 3- MonthsChange at 1-Month-.013 change in proportion of correct trialsStandard Deviation 0.04
HM - ChemotherapyChange From Baseline Filter Task Performance at 1- and 3- MonthsChange at 3-Month.002 change in proportion of correct trialsStandard Deviation 0.031
HM - No ChemotherapyChange From Baseline Filter Task Performance at 1- and 3- MonthsChange at 1-Month.011 change in proportion of correct trialsStandard Deviation 0.027
HM - No ChemotherapyChange From Baseline Filter Task Performance at 1- and 3- MonthsChange at 3-Month-.006 change in proportion of correct trialsStandard Deviation 0.025
Healthy ControlsChange From Baseline Filter Task Performance at 1- and 3- MonthsChange at 1-Month-.004 change in proportion of correct trialsStandard Deviation 0.034
Healthy ControlsChange From Baseline Filter Task Performance at 1- and 3- MonthsChange at 3-Month-.006 change in proportion of correct trialsStandard Deviation 0.028
Primary

Change From Baseline N2pc Amplitude at 1- and 3- Months

Electrophysiological component that measures allocation of attentional resources

Time frame: Collected at Study Induction, 1 month after Study Induction, 3 months after Study Induction

Population: Differences in number analyzed and numbers in the Participant Flow module were due to patient dropout and either: (1) a minority of patients requesting not to complete testing; or (2) difficulties with electrophysiology or stimulus presentation equipment.

ArmMeasureGroupValue (MEAN)Dispersion
HM - ChemotherapyChange From Baseline N2pc Amplitude at 1- and 3- MonthsChange at 1-Month.47 microvoltsStandard Deviation 0.77
HM - ChemotherapyChange From Baseline N2pc Amplitude at 1- and 3- MonthsChange at 3-Month.41 microvoltsStandard Deviation 1
HM - No ChemotherapyChange From Baseline N2pc Amplitude at 1- and 3- MonthsChange at 3-Month.36 microvoltsStandard Deviation 0.96
HM - No ChemotherapyChange From Baseline N2pc Amplitude at 1- and 3- MonthsChange at 1-Month-.02 microvoltsStandard Deviation 0.55
Healthy ControlsChange From Baseline N2pc Amplitude at 1- and 3- MonthsChange at 1-Month-.34 microvoltsStandard Deviation 0.54
Healthy ControlsChange From Baseline N2pc Amplitude at 1- and 3- MonthsChange at 3-Month-.63 microvoltsStandard Deviation 0.6

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026